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Record W2955862252 · doi:10.71781/12812

La communication organisationnelle et le rapport aux textes : comparaison des discours de deux Bureaux de l’intégration des immigrants à Genève et à Montréal

2018· dissertation· fr· W2955862252 on OpenAlexaboutno aff
Alizée Dermange

Bibliographic record

VenueOpen MIND · 2018
Typedissertation
Languagefr
FieldArts and Humanities
TopicLinguistics and Discourse Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtSociologyPhilosophyPolitical science

Abstract

fetched live from OpenAlex

Ce mémoire vise à étudier le rôle que jouent les textes dans la manière dont des acteurs organisationnels communiquent. Dans ce but, deux organisations gouvernementales ont été sélectionnées. Si elles sont localisées à Montréal et à Genève, elles répondent néanmoins à un but similaire : faciliter l’intégration des personnes immigrantes. L’objet de cette recherche est d’observer et analyser la manière dont des représentants de ces deux organisations se rapportent aux textes qui fondent juridiquement ou politiquement leur action. En particulier, cette étude reprend le concept de ventriloquie développé, depuis une dizaine d’années, par François Cooren (2010, 2012, 2013, 2014, 2015) afin de tenter de déceler les moments où les membres de ces organisations font parler ces textes, de manière plus ou moins intentionnelle, pour légitimer leur action. A quel type de textes ces phénomènes de ventriloquie se réfèrent-ils? De quelles manières? Et finalement, dans quels buts? C’est à ces questions que ce travail propose de répondre.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.257

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0140.013
Scholarly communication0.0110.004
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0120.001

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 head0.067
GPT teacher head0.352
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), 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
Published2018
Admission routes1
Has abstractyes

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