Bibliographic record
Abstract
Various communities, including those glossed as traditional, indigenous, subaltern, Afro-descended, tribal, or, in an older vocabulary, primitive, pre-modern, non-civilized, barbaric, and polluted, in particular contexts, can find themselves in a situation I have come to refer as underdogs. Underdog references communities which are positioned to access various levels of historical consciousness in order to mobilize their struggles against impinging, dominant, homogenizing forces. These forces are based in a colonial culture against which communities resist. The strategies of resistance both refer to their traditions and to their historic circumstances, as well as their recognition of historical fluidity which allows them the possibility of facing, encountering and rewriting their histories. This resistance has made them resilient. Ethnographic research conduced together with the quilombola community of Periperi, in the state of Piauí, Brazil; the neighbourhood of La Marina, in Matanzas, Cuba, and the Hwlitsum indigenous people, in British Columbia, Canada shows that these communities in an underdog situation cannot back off from their challenges to existing modes of power of their local and regional setting in their efforts to mitigate their status as polluted. Experiencing being in such situations throughout their trajectory has led these communities to a condition they have not been able to run from, albeit finding new ways to embrace it, in the underdog world they live in.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".