Organizational Discourse and Networks Mobilization: A Case Study of the “MigrActive Discourse” in Quebec (Canada)
Bibliographic record
Abstract
Mobilizing local and regional actors’ networks appears fundamental to the development of a territory plagued by many issues. Therefore the knowledge of mobilizing factors appears to be a necessity. Many of them have been identified, without really taking into account the logic of cooperation networks. Recently, interactions between actors who make up a unique network, appeared to have an ability to mobilize. More precisely, there are mobilizing influences within networks of actors. The best known are leadership and communicative influence. The latter is the object of study in this article. By means of their discourse, some organizations demonstrate their ability to alter the perceptions of regional actors, so they are able to mobilize for collective actions to develop their community. To demonstrate this, the case of the migrActive discourse was investigated. It was born at the initiative of youth organizations in the Saguenay - Lac-Saint-Jean in Quebec, which aimed to change the mindset of regional actors on the issue of youth migration. They wanted to introduce a positive discourse to create cohesion and regional mobilization, so that young people want to settle in the Saguenay - Lac-Saint-Jean.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".