Bibliographic record
Abstract
Sandra Kouritzin’s Word from the Editor in the last issue of TCJ is a refreshingly heartfelt and honest appraisal of her own take on the subject positions we occupy as second-language educators in terms of class, race, or (as is my case) gender. In our relatively privileged profession, we have ambiguous relationships with our learners, who more often than not hold disadvantaged positions in our society as recent immigrants. Like many in our profession, I struggle with the same questions. Britishbased culture, from which my own heritage draws, has been especially privileged in Canada. I have also been guilty of racist and sexist thought and conduct, much to my shame and consternation. However, like many of us in the profession (and I count Sandie Kouritzin in that number), I have actively engaged in antiracist work both in and out of the classroom. To my mind, the important thing about living in today’s world is what we do with the subject and material positions we inherit. Do we challenge patriarchal and hegemonic structures to the best of our ability? Are we vigilant in recognizing our own limitations and shortcomings so as to learn from our mistakes? And as educators do we build on this awareness to offer our students tools that can be used to combat poverty, exploitation, and discrimination? I believe that it is important not to group all privileged or disadvantaged peoples into the same amorphous blocks. Race is an ideological construct that is to a large extent part of imperialist projects (Ricento, 2000). In my opinion, it is best viewed in the light of recent theories related to identity construction (Norton, 2000). Despite what the religious and political fundamentalists in today’s world would have us believe, we are all complicated creatures who occupy various subject positions concurrently. We all change, exhibit contradictions, and perform acts that are at various times progressive or regressive. In my own work I am very much aware of my role as an ESL teacher in relationship to the nation state to which I belong. My students will become Canadian citizens, and like others in our profession I have an important role in their progress toward this goal. I continually ask myself if the vision of Canada that I present to my students is emancipatory. Am I replicating a
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.018 |
| Scholarly communication | 0.024 | 0.018 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.033 | 0.060 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".