Contestation about Collaboration: Discursive Boundary Work among Professions
Bibliographic record
Abstract
We examine how professions responded to a potential change in jurisdictional boundaries by analyzing the written submissions of five professional associations in reaction to a government proposal to strengthen interprofessional collaboration, relating these responses to the professions’ field positions. We identify four foci for framing used by the professions to discursively develop their boundary claims: (1) framing the issue of interprofessional collaboration (issue framing), (2) framing of justifications for favored solutions (justifying), (3) framing the profession’s own identity (self-casting), and (4) framing other professions’ identities (altercasting). We find that professions employed these foci differently depending on two dimensions of their field positions – status and centrality. Our study contributes to the literature by identifying distinctive ways through which the foci for framing may be mobilized in situations of boundary contestation, and by theorizing how field position in terms of status and centrality influences actors’ framing strategies.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.037 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.014 | 0.022 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".