Modeling the Evaluation Process in a Public Controversy
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".