Revisiting Latino/a identity using Census data

On April 10, I attended a talk by Jennifer Leeman (Research Sociolinguist @Census and Assistant Professor @George Mason) entitled “Spanish and Latino/a identity in the US Census.” This was a great talk. I’ll include the abstract below, but here are some of her main points:

  • Census categories promote and legitimize certain understandings, particularly because the Census, as a tool of the government, has an appearance of neutrality
  • Census must use categories from OMB
  • The distinction between race and ethnicity is fuzzy and full of history.
    • o   In the past, this category has been measured by surname, mothertongue, birthplace
      o   Treated as hereditary (“perpetual foreigner” status)
      o   Self-id new, before interviewer would judge, record
  • In the interview context, macro & micro meet
    • o   Macro level demographic categories
    • o   Micro:
      • Interactional participant roles
      • Indexed through labels & structure
      • Ascribed vs claimed identities
  • The study: 117 telephone interviews in Spanish
    • o   2 questions, ethnicity & race
    • o   Ethnicity includes Hispano, Latino, Español
      • Intended as synonyms but treated as a choice by respondents
      • Different categories than English (Adaptive design at work!)
  • The interviewers played a big role in the elicitation
    • o   Some interviewers emphasized standardization
      • This method functions differently in different conversational contexts
    • o   Some interviewers provided “teaching moments” or on-the-fly definitions
      • Official discourses mediated through interviewer ideologies
      • Definitions vary
  • Race question also problematic
    • o   Different conceptions of Indioamericana
      • Central, South or North American?
  • Role of language
    • o   Assumption of monolinguality problematic, bilingual and multilingual quite common, partial and mixed language resources
    • o   “White” spoken in English different from “white” spoken in Spanish
    • o   Length of time in country, generation in country belies fluid borders
  • Coding process
    • o   Coding responses such as “American, born here”
    • o   ~40% Latino say “other”
    • o   Other category ~ 90% Hispanic (after recoding)
  • So:
    • o   Likely result: one “check all that apply” question
      • People don’t read help texts
    • o   Inherent belief that there is an ideal question out there with “all the right categories”
      • Leeman is not yet ready to believe this
    • o   The takeaway for survey researchers:
      • Carefully consider what you’re asking, how you’re asking it and what information you’re trying to collect
  • See also Pew Hispanic Center report on Latino/a identity

 

 

 ABSTRACT

Censuses play a crucial role in the institutionalization and circulation of specific constructions of national identity, national belonging, and social difference, and they are a key site for the production and institutionalization of racial discourse (Anderson 1991; Kertzer & Arel 2002; Nobles 2000; Urla 1994).  With the recent growth in the Latina/o population, there has been increased interest in the official construction of the “Hispanic/Latino/Spanish origin” category (e.g., Rodriguez 2000; Rumbaut 2006; Haney López 2005).  However, the role of language in ethnoracial classification has been largely overlooked (Leeman 2004). So too, little attention has been paid to the processes by which the official classifications become public understandings of ethnoracial difference, or to the ways in which immigrants are interpellated into new racial subjectivities.

This presentation addresses these gaps by examining the ideological role of Spanish in the history of US Census Bureau’s classifications of Latina/os as well as in the official construction of the current “Hispanic/Latino/Spanish origin” category. Further, in order to gain a better understanding of the role of the census-taking in the production of new subjectivities, I analyze Spanish-language telephone interviews conducted as part of Census 2010.  Insights from recent sociocultural research on the language and identity (Bucholtz and Hall 2005) inform my analysis of how racial identities are instantiated and negotiated, and how respondents alternatively resist and take up the identities ascribed to them.

* Dr. Leeman is a Department of Spanish & Portuguese Graduate (GSAS 2000).

Total Survey Error: nanny to some, wise elder for some, strange parental friend for others

Total Survey Error and I are long-time acquaintences, just getting to know each other better. Looking at TSE is, for me, like looking at my work in survey research through a distorted mirror to an alternate universe. This week, I’ve spent some time closely reading Groves’ Past, Present and Future of Total Survey Error, and it provided some historical context to the framework, as well as an experienced account of its strengths and weaknesses.

Errors are an important area of study across many fields. Historically, models about error assumed that people didn’t really make errors often. Those attitudes are alive and well in many fields and workplaces today. Instead of carefully considering errors, they are often dismissed as indicators of incompetence. However, some workplaces are changing the way they approach errors. I did some collaborative research on medical errors in 2012 and was introduced to the term HRO or High-Reliability Organization. This is an error focused model of management that assumes that errors will be made, and not all errors can be anticipated. Therefore, every error should be embraced as a learning opportunity to build a better organizational framework.

From time to time, various members of our working group have been driven to create checklists for particular aspects of our work. In my experience, the checklists are very helpful for work that we do infrequently and virtually useless for work that we do daily. Writing a checklist for your daily work is a bit like writing instructions on how you brush your teeth and expecting to keep those instructions updated whenever you make a change of sorts. Undoubtedly, you’ll reread the instructions and wonder when you switched from a vertical to a circular motion for a given tooth. And yet there are so many important elements to our work, and so many areas where people could make less than ideal decisions (small or large). From this need rose Deming, with the first survey quality checklist. After Deming, a few other models arose. Eventually, TSE became the cumulative working framework or foundational framework for the field of survey research.

In my last blog, I spoke about the strangeness of coming across a foundational framework after working in the field without one. The framework is a conceptually important one, separating out sources of errors in ways that make shortcomings and strengths apparent and clarifying what is more or less known about a project.

But in practice, this model has not become the applied working model that its founders and biggest proponents expected it to be. This is for two reasons (that I’ll focus on), one of which Groves mentioned in some detail in this paper and one of which he barely touched on (but likely drove him out of the field).

1. The framework has mathematical properties, and this has led to its more intensive use on aspects of the survey process that are traditionally quantitative. TSE research in areas of sampling, coverage, response and aspects of analysis is quite common, but TSE research in other areas is much less common. In fact, many of the less quantifiable parts of the survey process are almost dismissed in favor of the more quantifiable parts. A survey with a particularly low TSE value could have huge underlying problems or be of minimal use once complete.
2. The framework doesn’t explicitly consider the human factors that govern research behind the scenes. Groves mentioned that the end users of the data are not deeply considered in the model, but neither are the other financial and personal (and personafinancial) constraints that govern much decision making. Ideally, the end goal of research is high quality research that yields a useful and relevant response for as minimal cost as possible. In practice, however, the goal is both to keep costs low and to satisfy a system of interrelated (and often conflicting) personal or professional (personaprofessional?) interests. If the most influential of these interests are not particularly interested in (or appreciative of) the model, practitioners are highly unlikely to take the time to apply it.

Survey research requires very close attention to detail in order to minimize errors. It requires an intimate working knowledge of math and of computer programming. It also benefits from a knowledge of human behavior and the research environment. If I were to recommend any changes to the TSE model, I would recommend a bit more task based detail, to incorporate more of the highly valued working knowledge that is often inherent and unspoken in the training of new researchers. I would also recommend a more of an HRO orientation toward error, anticipating and embracing unexpected errors as a source of additions to the model. And I would recommend some deeper incorporation of the personal and financial constraints and the roles they play (clearly an easier change to introduce than to flesh out in any great detail!). I would recommend a shift of focus, away from the quantitative modeling aspects and to the overall applicability and importance of a detailed, applied working model.

I’ve suggested before that survey research does not have a strong enough public face for the general public to understand or deeply value our work. A model that is better embraced by the field could for the basis for a public face, but the model would have to appeal to practitioners on a practical level. The question is: how do you get members of a well established field who have long been working within it and gaining expertise to accept a framework that grew into a foundational piece independent of their work?

Total Survey Error: as Iconic as the Statue of Liberty herself?

In Jan Blommaerts book, the Sociolinguistics of Globalization, I learned about the iconicity of language. Languages, dialects, phrases and words have the potential to be as iconic as the statue of liberty. As I read Blommaert’s book, I am also reading about Total Survey Error, which I believe to be an iconic concept in the field of survey research.

Total Survey Error (TSE) is a relatively new, albeit very comprehensive framework for evaluating a host of potential error sources in survey research. It is often mentioned by AAPOR members (national and local), at JPSM classes and events, and across many other events, publications and classes for survey researchers. But here’s the catch: TSE came about after many of us entered the field. In fact, by the time TSE debuted and caught on as a conceptual framework, many people had already been working in the field for long enough that a framework didn’t seem necessary or applicable.

In the past, survey research was a field that people grew into. There were no degree or certificate programs in survey research. People entered the field from a variety of educational and professional backgrounds and worked their way up through the ranks from data entry, coder or interviewing positions to research assistant and analyst positions, and eventually up to management. Survey research was a field that valued experience, and much of the essential job knowledge came about through experience. This structure strongly characterizes my own office, where the average tenure is fast approaching two decades. The technical and procedural history of the department is alive and well in our collections of artifacts and shared stories. We do our work with ease, because we know the work well, and the team works together smoothly because of our extensive history together. Challenges or questions are an opportunity for remembering past experiences.

Programs such as the Joint Program in Survey Methodology (JPSM, a joint venture between the University of Michigan and University of Maryland) are relatively new, arising, for the most part, once many survey researchers were well established into their routines. Scholarly writings and journals multiplied with the rise of the academic programs. New terms and new methods sprang up. The field gained an alternate mode of entry.

In sociolinguistics, we study evidentiality, because people value different forms of evidence. Toward this end, I did a small study of survey researchers’ language use and mode of evidentials and discovered a very stark split between those that used experience to back up claims and those who relied on research to back up claims. This stark difference matched up well to my own experiences. In fact, when I coach jobseekers who are looking for survey research positions, I  draw on this distinction and recommend that they carefully listen to the types of evidentials they hear from the people interviewing them and try to provide evidence in the same format. The divide may not be visible from the outside of the field, but it is a strong underlying theme within it.

The divide is not immediately visible from the outside because the face of the field is formed by academic and professional institutions that readily embrace the academic terminology. The people who participate in these institutions and organizations tend to be long term participants who have been exposed to the new concepts through past events and efforts.

But I wonder sometimes whether the overwhelming public orientation to these methods doesn’t act to exclude some longtime survey researchers in some ways. I wonder whether some excellent knowledge and history get swept away with the new. I wonder whether institutions that represent survey research represent the field as a whole. I wonder what portion of the field is silent, unrepresented or less connected to collective resources and changes.

Particularly as the field encounters a new set of challenges, I wonder how well prepared the field will be- not just those who have been following these developments closely, but also those who have continued steadfast, strong, and with limited errors- not due to TSE adherence, but due to the strength of their experience. To me, the Total Survey Error Method is a powerful symbol of the changes afoot in the field.

For further reference, I’m including a past AAPOR presidential address by Robert Groves

groves aapor

Proceedings of the Fifty-First Annual Conference of the American Association for Public Opinion Research
Source: Source: The Public Opinion Quarterly, Vol. 60, No. 3 (Autumn, 1996), pp. 471-513
ETA other references:

Bob Groves: The Past, Present and Future of Total Survey Error

Slideshow summary of above article

Is there Interdisciplinary hope for Social Media Research?

I’ve been trying to wrap my head around social media research for a couple of years now. I don’t think it would be as hard to understand from any one academic or professional perspective, but, from an interdisciplinary standpoint, the variety of perspectives and the disconnects between them are stunning.

In the academic realm:

There is the computer science approach to social media research. From this standpoint, we see the fleshing out of machine learning algorithms in a stunning horserace of code development across a few programming languages. This is the most likely to be opaque, proprietary knowledge.

There is the NLP or linguistic approach, which overlaps to some degree with the cs approach, although it is often more closely tied to grammatical rules. In this case, we see grammatical parsers, dictionary development, and api’s or shared programming modules, such as NLTK or GATE. Linguistics is divided as a discipline, and many of these divisions have filtered into NLP.

Both the NLP and CS approaches can be fleshed out, trained, or used on just about any data set.

There are the discourse approaches. Discourse is an area of linguistics concerned with meaning above the level of the sentence. This type of research can follow more of a strict Conversation Analysis approach or a kind of Netnography approach. This school of thought is more concerned with context as a determiner or shaper of meaning than the two approaches above.

For these approaches, the dataset cannot just come from anywhere. The analyst should understand where the data came from.

One could divide these traditions by programming skills, but there are enough of us who do work on both sides that the distinction is superficial. Although, generally speaker, the deeper one’s programming or qualitative skills, the less likely one is to cross over to the other side.

There is also a growing tradition of data science, which is primarily quantitative. Although I have some statistical background and work with quantitative data sets every day, I don’t have a good understanding of data science as a discipline. I assume that the growing field of data visualization would fall into this camp.

In the professional realm:

There are many companies in horseraces to develop the best systems first. These companies use catchphrases like “big data” and “social media firehose” and often focus on sentiment analysis or topic analysis (usually topics are gleaned through keywords). These companies primarily market to the advertising industry and market researchers, often with inflated claims of accuracy, which are possible because of the opacity of their methods.

There is the realm of market research, which is quickly becoming dependent on fast, widely available knowledge. This knowledge is usually gleaned through companies involved in the horserace, without much awareness of the methodology. There is an increasing need for companies to be aware of their brand’s mentions and interactions online, in real time, and as they collect this information it is easy, convenient and cost effective to collect more information in the process, such as sentiment analyses and topic analyses. This field has created an astronomically high demand for big data analysis.

There is the traditional field of survey research. This field is methodical and error focused. Knowledge is created empirically and evaluated critically. Every aspect of the survey process is highly researched and understood in great depth, so new methods are greeted with a natural skepticism. Although they have traditionally been the anchors of good professional research methods and the leaders in the research field, survey researchers are largely outside of the big data rush. Survey researchers tend to value accuracy over timeliness, so the big, fast world of big data, with its dubious ability to create representative samples, hold little allure or relevance.

The wider picture

In the wider picture, we have discussions of access and use. We see a growing proportion of the population coming online on an ever greater variety of devices. On the surface, the digital divide is fast shrinking (albeit still significant). Some of the digital access debate has been expanded into an understanding of differential use- essentially that different people do different activities while online. I want to take this debate further by focusing on discursive access or the digital representation of language ideologies.

The problem

The problem with such a wide spread of methods, needs, focuses and analytic traditions is that there isn’t enough crossover. It is very difficult to find work that spreads across these domains. The audiences are different, the needs are different, the abilities are different, and the professional visions are dramatically different across traditions. Although many people are speaking, it seems like people are largely speaking within silos or echo chambers, and knowledge simply isn’t trickling across borders.

This problem has rapidly grown because the underlying professional industries have quickly calcified. Sentiment analysis is not the revolutionary answer to the text analysis problem, but it is good enough for now, and it is skyrocketing in use. Academia is moving too slow for the demands of industry and not addressing the needs of industry, so other analytic techniques are not being adopted.

Social media analysis would best be accomplished by a team of people, each with different training. But it is not developing that way. And that, I believe, is a big (and fast growing) problem.

Notes on the Past, Present and Future of Survey Methodology from #dcaapor

I had wanted to write these notes up into paragraphs, but I think the notes will be more timely, relevant and readable if I share them as they are. This was a really great conference- very relevant and timely- based on a really great issue of Public Opinion Quarterly. As I was reminded at the DC African Festival (a great festival, lots of fun, highly recommended) on Saturday, “In order to understand the future you must embrace the past.”

DC AAPOR Annual Public Opinion Quarterly Special Issue Conference

75th Anniversary Edition

The Past, Present and Future of Survey Methodology and Public Opinion Research

Look out for slides from the event here: http://www.dc-aapor.org/pastevents.php

 

Note: Of course, I took more notes in some sessions than others…

Peter Miller:

–       Adaptive design- tracking changes in estimates across mailing waves and tracking response bias, is becoming standard practice at Census

–       Check out Howard Schuman’s article tracking attitudes toward Christopher Columbus

  • Ended up doing some field research in the public library, reading children’s books

Stanley Presser:

–       Findings have no meaning independent of the method with which they were collected

–       Balance of substance and method make POQ unique (this was a repeated theme)

Robert Groves:

–       The survey was the most important invention in Social Science in the 20th century – quote credit?

–       3 era’s of Survey research (boundaries somewhat arbritrary)

  • 1930-1960
    • Foundation laid, practical development
  • 1960-1990
    • Founders pass on their survey endeavors to their protégés
    • From face to face to phone and computer methods
    • Emergence & Dominance of Dillman method
    • Growth of methodological research
    • Total Survey Error perspective dominates
    • Big increase in federal surveys
    • Expansion of survey centers & private sector organizations
    • Some articles say survey method dying because of nonresponse and inflating costs. This is a perennial debate. Groves speculated that around every big election time, someone finds it in their interest to doubt the polls and assigns a jr reporter to write a piece calling the polls into question.
  • 1990à
    • Influence of other fields, such as social cognitive psychology
    • Nonresponse up, costs up à volunteer panels
    • Mobile phones decrease cost effectiveness of phone surveys
    • Rise of internet only survey groups
    • Increase in surveys
    • Organizational/ business/ management skills more influential than science/ scientists
    • Now: software platforms, culture clash with all sides saying “Who are these people? Why do they talk so funny? Why don’t they know what we know?”
    • Future
      • Rise of organic data
      • Use of administrative data
      • Combining data sets
      • Proprietary data sets
      • Multi-mode
      • More statistical gymnastics

Mike Brick:

  • Society’s demand for information is Insatiable
  • Re: Heckathorn/ Respondent Driven samples
    • Adaptive/ indirect sampling is better
    • Model based methods
      • Missing data problem
      • Cost the main driver now
      • Estimation methods
      • Future
        • Rise of multi-frame surveys
        • Administrative records
        • Sampling theory w/nonsampling errors at design & data collection stages
          • Sample allocation
          • Responsive & adaptive design
          • Undercoverage bias can’t be fixed at the back end
            • *Biggest problem we face*
            • Worse than nonresponse
            • Doug Rivers (2007)
              • Math sampling
              • Web & volunteer samples
              • 1st shot at a theory of nonprobability sampling
            • Quota sampling failed in 2 high profile examples
              • Problem: sample from interviews/ biased
              • But that’s FIXABLE
            • Observational
              • Case control & eval studies
              • Focus on single treatment effect
              • “tougher to measure everything than to measure one thing”

Mick Couper:

–       Mode an outdated concept

  • Too much variety and complexity
  • Modes are multidimensional
    • Degree of interviewer involvement
    • Degree of contact
    • Channels of communication
    • Level of privacy
    • Technology (used by whom?)
    • Synchronous vs. asynchronous
  • More important to look at dimensions other than mode
  • Mode is an attribute of a respondent or item
  • Basic assumption of mixed mode is that there is no difference in responses by mode, but this is NOT true
    • We know of many documented, nonignorable, nonexplainable mode differences
    • Not “the emperor has no clothes” but “the emperor is wearing suggestive clothes”
    • Dilemma: differences not Well understood
      • Sometimes theory comes after facts
      • That’s where we are now- waiting for the theory to catch up (like where we are on nonprobability sampling)

–       So, the case for mixed mode collection so far is mixed

  • Mail w/web option has been shown to have a lower response rate than mail only across 24-26 studies, at least!!
    • (including Dillman, JPSM, …)
    • Why? What can we do to fix this?
    • Sequential modes?
      • Evidence is really mixed
      • The impetus for this is more cost than response rate
      • No evidence that it brings in a better mix of people

–       What about Organic data?

  • Cheap, easily available
  • But good?
  • Disadvantages:
    • One var at a time
    • No covariates
    • Stability of estimates over time?
    • Potential for mischief
      • E.g. open or call-in polls
      • My e.g. #muslimrage
  • Organic data wide, thin
  • Survey data narrow, deep

–       Face to face

  • Benchmark, gold standard, increasingly rare

–       Interviewers

  • Especially helpful in some cases
    • Nonobservation
    • Explaining, clarifying

–       Future

  • Technical changes will drive dev’t
  • Modes and combinations of modes will proliferate
  • Selection bias The Biggest Threat
  • Further proliferation of surveys
    • Difficult for us to distinguish our work from “any idiot out there doing them”

–       Surveys are tools for democracy

  • Shouldn’t be restricted to tools for the elite
  • BUT
  • There have to be some minimum standards

–       “Surveys are tools and methodologists are the toolmakers”

Nora Cate Schaeffer:

–       Jen Dykema read & summarized 78 design papers- her summary is available in the appendix of the paper

–       Dynamic interactive displays for respondent in order to help collect complex data

–       Making decisions when writing questions

  • See flow chart in paper
    • Some decisions are nested
  • Question characteristics
    • E.g. presence or absence of a feature
      • E.g. response choices

Sunshine Hillygus:

–       Political polling is “a bit of a bar trick”

  • The best value in polls is in understanding why the election went the way it did

–       Final note: “The things we know as a field are going to be important going forward, even if it’s not in the way they’ve been used in the past”

Lori Young and Diana Mutz:

–       Biggest issues:

  • Diversity
  • Selective exposure
  • Interpersonal communication

–       2 kinds of search, influence of each

  • Collaborative filter matching, like Amazon
    • Political targeting
    • Contentious issue: 80% of people said that if they knew a politician was targeting them they wouldn’t vote for that candidate
      • My note: interesting to think about peoples relationships with their superficial categories of identity- it’s taken for granted so much in social science research, yet not by the people within the categories

–       Search engines: the new gatekeepers

  • Page rank & other algorithms
  • No one knows what influence personalization of search results will have
  • Study on search learning: gave systematically different input to train engines are (given same start point), results changes Fast and Substantively

Rob Santos:

–       Necessity mother of invention

  • Economic pressure
  • Reduce costs
  • Entrepreneurial spirit
  • Profit
  • Societal changes
    • Demographic diversification
      • Globalization
      • Multi-lingual
      • Multi-cultural
      • Privacy concerns
      • Declining participation

–       Bottom line: we adapt. Our industry Always Evolves

–       We’re “in the midst of a renaissance, reinventing ourselves”

  • Me: That’s framing for you! Wow!

–       On the rise:

  • Big Data
  • Synthetic Data
    • Transportation industry
    • Census
    • Simulation studies
      • E.g. How many people would pay x amount of income tax under y policy?
  • Bayesian Methods
    • Apply to probability and nonprobability samples
  • New generation
    • Accustomed to and EXPECT rapid technological turnover
    • Fully enmeshed in social media

–       3 big changes:

  • Non-probability sampling
    • “Train already left the station”
    • Level of sophistication varies
    • Model based inference
    • Wide public acceptance
    • Already a proliferation
  • Communication technology
    • Passive data collection
      • Behaviors
        • E.g. pos (point of service) apps
        • Attitudes or opinions
      • Real time collection
        • Prompted recall (apps)
        • Burden reduction
          • Gamification
  • Big Data
    • What is it?
    • Data too big to store
      • (me: “think “firehoses”)
      • Volume, velocity, variety
      • Fuzzy inferences
      • Not necessarily statistical
      • Coursenes insights

–       We need to ask tough questions

  • (theme of next AAPOR conference is just that)
  • We need to question probability samples, too
    • Flawed designs abound
    • High nonresponse & noncoverage
    • Can’t just scrutinize nonprobability samples
  • Nonprobability designs
    • Some good, well accepted methods
    • Diagnostics for measurement
      • How to measure validity?
      • What are the clues?
      • How to create a research agenda to establish validity?
  • Expanding the players
    • Multidisciplinary
      • Substantive scientists
      • Math stats
      • Modelers
      • Econometricians
  • We need
    • Conversations with practitioners
    • Better listening skills

–       AAPOR’s role

  • Create forum for conversation
  • Encourage transparency
  • Engage in outreach
  • Understanding limitations but learning approaches

–       We need to explore the utility of nonprobability samples

–       Insight doesn’t have to be purely from statistical inferences

–       The biggest players in big data to date include:

  • Computational scientists
  • Modelers/ synthetic data’ers

–       We are not a “one size fits all” society, and our research tools should reflect that

My big questions:

–       “What are the borders of our field?”

–       “What makes us who we are, if we don’t do surveys even primarily?”

Linguistic notes:

–       Use of we/who/us

–       Metaphors: “harvest” “firehose”

–       Use of specialized vocabulary

–       Use of the word “comfortable”

–       Interview as a service encounter?

Other notes:

–       This reminds me of Colm O’Muircheartaigh- from that old JPSM distinguished lecture

  • Embracing diversity
  • Allowing noise
  • Encouraging mixed methods

I wish his voice was a part of this discussion…

Remotely following AAPOR conference #aapor

The AAPOR 2012 conference began today in sunny Orlando, Florida. This is my my favorite conference of the year, and I am sorry to miss it. Fortunately, the Twitter action is bringing a lot of the action to homeviewers like us!

https://twitter.com/#!/search/realtime/%23AAPOR

I will keep retweeting some of the action. For those of you who may be concerned that this represents a new era of heavy tweeting for me, rest assured- it wont!

And for anyone who has been wondering what happened to me and my blog, please stay tuned. I am working on an exciting new project that I will eagerly share about in due time.