Métissage – somewhere between hope and happening
Bibliographic record
Abstract
Purpose Within minutes, our group, who had no prior introduction, began to learn the value of emerging from a relational space of (re)presenting and (re)storying our experiences. “I” became “We”, which became “Us” Design/methodology/approach Recently, the seventh bi-annual conference of the World Federation for Teacher Education 2023 focused on the theme of “Re-imagining Teacher Education: From Words to Action.” During the session on métissage as methodology, participants from four different countries, three ethnic backgrounds and gender and sexual differences were invited into dialogue to explore the nuances of our identities, academic positions and life experiences. Findings Doing métissage as novices, our subsequent discussion problematized the perpetuation of procedural narratives that contested the Cartesian cuts of methodological normalcy. Originality/value Sharing our stories of self in our group we referenced how institutional frameworks had shaped and were reconstructing contexts for our being and belonging in the academy. In our narrating vulnerability we were once again located in telling relations to do with identity, power and social being. Jones (2015, p. 8) has asked. “Other than dry academic reports, how can we retell these stories in sensitive and ethical ways to wider audiences? How do the stories themselves inspire creativity in retelling them? How can we involve participants in the retelling of their stories?”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.022 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".