Critical Black Futurism: Affecting Affinities within Curriculum Studies
Bibliographic record
Abstract
In this concept essay, I attempt to amplify this sense of critical futurity through my reading of scholars and teachers attempting to imagine beyond the past and present of what Henry Giroux (2012) has called the “age of disposability” of students’ futures in schooling and wider society, which could easily be read as also the age of dis-possibility. Within such a dystopian moment, I read and write in increasing awareness of my position as a White curriculum studies scholar in a rather White Canadian university – becoming aware that there is a distinct differential to whose futures are made disposable by the ways of “beingknowingdoing that strongly contextualize not only everyday interactions but also the understandings that undergird policies and programs” in education, yet nevertheless remain largely unexamined in social justice scholarship, even as such practices might reinforce marginalization of those they are in ostensible allyship (Gershon, 2020, p. 61; 53).
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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 | 0.024 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.043 | 0.107 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".