Participatory Action Research: A Gateway to the Professionalization of Emerging Scholars
Bibliographic record
Abstract
Graduate students and novice researchers face various challenges in their study programs or workplaces, including a research-focused curriculum and high research expectations at the expense of other areas of responsibility that would allow for training and socializing in their environment. The involvement in participatory action research (PAR) is a lever for supporting the professionalization of apprentice and novice researchers by promoting their training through the development of skills adapted to individual circumstances and by fostering their socialization in the academic environment. The results of the analysis of 63 reflective logbooks, two focus groups, and 20 individual interviews show professionalization in both areas of training and socializing. More specifically, the four professional skills that were most developed are project management, collaboration, digital, media, and information literacy, and communication. In terms of socialization, the experience of performing research differently, the implementation of horizontal governance, the varied distribution of responsibilities among participants, the work in multidisciplinary teams, and the hands-on learning of the PAR process played a decisive role. A discussion follows on the potential of PAR for the professionalization of emerging scholars, focusing on the strengths and distinctive features of their experience.
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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 | 0.200 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.043 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".