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Record W2793371598 · doi:10.1177/0170840617747918

Modeling the Evaluation Process in a Public Controversy

2018· article· en· W2793371598 on OpenAlexaffabout
Karl-Emanuel Dionne, Chantale Mailhot, Ann Langley

Bibliographic record

VenueOrganization Studies · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsProcess (computing)Perspective (graphical)Object (grammar)SociologyEpistemologyPublic relationsTest (biology)Positive economicsPolitical scienceEconomicsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Public controversies have attracted increasing attention in the organization studies literature. They emerge when critical issues are not defined and understood in the same way by different stakeholders, influencing the way they evaluate the worth of other actors, objects, and situations. In this paper, we show how the “orders of worth” perspective of Boltanski and Thévenot may throw light on the evolution of an evaluation process occurring during a public controversy. In particular, we study the Quebec student conflict of 2011 and 2012 that followed a proposed major increase in higher education tuition fees. We conducted an in-depth case study based on media coverage of the actions and discourses of the major actors to examine how objects and actions associated with a controversy are successively defined, redefined, and evaluated over time through a series of tests of worth. Our article contributes to the organizational literature on public controversies by drawing attention to the role of six types of evaluative moves in situations of controversy, and by offering an abductively developed model for understanding the evaluation process as it evolves over time. We suggest that actors, through these evaluative moves, may displace the object of a test, and therefore the foci for evaluation, through actions intended to bolster their positions.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
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 head0.071
GPT teacher head0.302
Teacher spread0.231 · 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 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

Citations22
Published2018
Admission routes2
Has abstractyes

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