Fierce Love: Fashioning Becoming a Researcher | Amour féroce : le façonnage d’un chercheur en devenir
Bibliographic record
Abstract
Abstract: This paper explores problematic discourses associated with becoming a researcher in the academy through the arts based research methods of autoethnography and narrative performance. I attempt to enhance awareness and contribute to growing knowledge surrounding the experience of becoming a researcher via fragments of my coming out journey, my experience as a psychotherapist and the evolution of my view of a researcher. Through performance-based frameworks and personal evocative writing toward inquiry I weave specific interactions with theoretical positioning. I highlight the collision of discourses and how both vulnerability and critical reflection paved the way in my becoming a researcher.Key Words: Arts based Research; Education; Researcher Identity Development; Autoethnography; Narrative Performance; Queer; LGBTQ; Microaggressions, CounsellingRésumé : Cet article traite des discours problématiques pour ceux et celles qui accèdent au monde universitaire en devenant des chercheurs par le biais de méthodes de recherche à caractère artistique, dont l’auto-ethnographie et la performance narrative. Je tente de stimuler la prise de conscience et de contribuer à l’expansion des connaissances entourant l’expérience du chercheur par l’entremise de fragments de mon cheminement d’affirmation de mon identité, de mon expérience de psychothérapeute et de l’évolution de ma perception de ce qu’est un chercheur. J’y entrecroise certaines interactions avec le positionnement théorique à l’aide de cadres performantiels et d’écriture personnelle évocatrice. Je mets en évidence la collision entre les discours et la façon dont tant la vulnérabilité que la réflexion critique ont jeté les bases du chercheur en devenir. Mots-clés : recherche artistique ; éducation ; développement de l’identité de chercheur ; auto-ethnographie ; performance narrative ; allosexuel ; LGBQ ; micro-agressions ; counseling
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.045 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.009 |
| 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".