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Record W2115363132 · doi:10.7202/1012750ar

Gatekeeping Practices in Interpreted Social Service Encounters

2012· article· en· W2115363132 on OpenAlexvenueno aff
Sonja Pöllabauer

Bibliographic record

VenueMeta Journal des traducteurs · 2012
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsGatekeepingGermanInterpreterSocial WelfarePublic relationsService (business)SociologySocial workWelfareSocial psychologyPsychologyPolitical scienceComputer scienceBusinessLinguisticsLawMarketing

Abstract

fetched live from OpenAlex

This paper presents results gathered from a project implemented by an interdisciplinary project team between 2007 and 2009, which focused on interpreting in social service and welfare institutions ( Community Interpreting und Kommunikationsqualität im Sozial- und Gesundheitswesen [Community Interpreting and Communication Quality in Social Service and Healthcare Institutions] ). One of the aspects investigated by the project was the interpreting practice at two Austrian municipal social service and welfare institutions via in-depth interviews and recordings of authentic interpreter-mediated encounters. After a brief overview of the history of gatekeeping theory and the application of the gatekeeping concept in Translation and Interpreting Studies, some of the project results are analysed using one specific model of gatekeeping theory proposed by Shoemaker and Vos in 2009. Taking a leaf from this work, the analysis is based on five different levels, namely the individual level, communication routines, the organisation level, the social institutional level, and the social system level. The analysis investigates “gates” present in the communication routines at the two institutions and which may prevent non-German speaking clients from full access and understanding, as well as the role of interpreters as “gatekeepers.”

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.204
GPT teacher head0.474
Teacher spread0.269 · 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.

Study designObservational
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

Citations14
Published2012
Admission routes1
Has abstractyes

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