The Double Stigma Challenge: How Blocklisted \nColleges from Montreal are Surviving After Fraud Accusations in 2020
Bibliographic record
Abstract
This study investigates how organizations overcome stigmatization attributed to their disapproved activities and amplified by negative events. Through a comparative case study of two out of ten sanctioned colleges in Montreal, and, by comparing the ongoing trajectories of both these colleges in managing their stigmas, I’ve investigated the strategies they adopted in response to their main audiences in order to survive. Using various data sources, including press articles, government publications, public company information, and interviews, this study shows that colleges followed different strategies according to their stigma intensity. The college with high stigmatization intensity followed a stigma containment strategy, focusing on actions to strengthen relationships with their allies, the students and partner-employers, creating a virtuous cycle within this group. The college with low stigmatization intensity followed a destigmatization strategy, focusing on collective action with other colleges through the provincial and national private college associations. This was achieved by actions that were mainly political, aiming to change the stigmatizer's perspective, the government, in their favor. This work contributes to the literature on organizational stigma, as well as discussions on legitimacy and reputation. It explores how different intensities of stigmas demand different strategies for similar institutions and proposes understanding how core and event stigmas interact, intensifying or reducing each other. The study also contributes to managerial practice by explicating strategies that stigmatized organizations can use.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".