The Politics of “Being and Becoming” a Researcher: Identity, Power, and Negotiating the Field
Bibliographic record
Abstract
This article explores the methodological turning points in conducting a critical ethnography on the discursive practices of Italian Canadian youth identities across their multiple worlds in Toronto (cf. Giampapa, 2004a Giampapa, F. 2004a. “The politics of identity, representation, and the discourses of self-identification: Negotiating the periphery and the center”. In Negotiation of identities in multilingual context, Edited by: Pavlenko, A. and Blackledge, A. 192–218. Clevedon, , UK: Multilingual Matters. [Crossref] , [Google Scholar]). Specifically, I aim to problematize the construction of the “researcher,” researcher identities, and the conceptualization of “insider/outsider” in relation to “being in the field.” I hope to move beyond a prescriptive view of the researcher in the field and to critically reveal the ways in which researcher identities are constructed through the social practices and discourses in which we are embedded as we conduct critical ethnographic research. In my research I became implicated in the debates and social constructions of being and becoming Italian Canadian in Toronto. My shifting identities and positionalities became part of a delicate dance across the research sites where participants exercised their power in ways that would open or close doors in the field.
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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 | 0.064 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.054 | 0.246 |
| Scholarly communication | 0.029 | 0.015 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".