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Record W7001087087

Immigrant Bloggers and Sensemaking: Technology Mediated Acculturation and Cultural Brokerage

2012· article· en· W7001087087 on OpenAlexaffabout

Bibliographic record

VenueMaynooth University ePrints and eTheses Archive (Maynooth University) · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsCarleton University
Fundersnot available
KeywordsAcculturationSensemakingSociocultural evolutionPopularityNetnographyExpatriateConversationImmigrationCultural diversitySocial media
DOInot available

Abstract

fetched live from OpenAlex

Millions of people are engaged in work abroad every year either through migration, expatriation or short overseas assignments. Understanding how people deal with foreign cultures and contexts is of key importance for individuals and organizations employing them, and has been the focus of management research since the late 1970’s (see Black, Mendenhall & Oddou, 1991 for a review). A common theme in the expatriate and cultural adjustment literature is the importance of cultural training and cultural translators in facilitating the transition into new cultures (e.g. Soloman, 1994; Janssens, 1995; Steers, Nardon & Sanchez-Runde, in press).
\nFor the purpose of this research we conceptualize acculturation as a process of sensemaking and focus on the role played by sociocultural brokers (Glanz, Williams & Hoekesema, 2001). Sociocultural brokers are individuals who distribute knowledge, and that are able to bridge cultures and influence interpretation between cultures. Through personal accounts
\nand stories, cultural brokers develop relationships and help individuals to make sense of discrepancies or problems and identify solutions and appropriate actions.
\nThe emergence and growing popularity of social media in general, and blogs in particular, have provided sojourner and migrants new mechanisms to acquire and share cultural information. A blogs is a website or part of a website were people post material on a regular basis. A blog is the equivalent of an online diary except that it is meant for public distribution and stimulates a conversation with other likeminded individuals. The number of blogs is growing exponentially, and it was estimated in November of 2011 to be at about 175 million (Pensato, 2011). Initially, bloggers used blogs as a mean to communicate with friends and family, but increasingly, bloggers are using blogs to communicate with other bloggers (Technorati, 2011). These conversations generate virtual communities as bloggers comment on and provide links to other blogs.
\nThe blogs of immigrants offer a window into the process of acculturation. They provide longitudinal, rich, present tense accounts of every day events and reflections, which are the foundation of sensemaking and acculturation. Thus, immigrant blogs provide a unique and valuable data source for studying acculturation.
\nWe conceptualize immigrant and expatriate bloggers as a new generation of sociocultural brokers. New media provides these blogging sociocultural brokers with different tools provided such as interactivity, connectivity, multimedia (not only text, but also images, movies and audio), and immediacy. These blogging sociocultural brokers also have the potential to reach a significantly broader audience than their traditional counterparts. Despite their increasing importance, we know very little about how they operate and their potential impact in the acculturation process.
\nIn this study we investigate how blogging technology influences the process of acculturation and cultural brokerage through an analysis of blogs written by foreign individuals living in Canada. Our objective is to develop a process model of technology mediated acculturation and cultural brokerage.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.261
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2012
Admission routes2
Has abstractyes

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