Bibliographic record
Abstract
In this chapter, I describe the approaches used in this study and argue for the necessity of a postcolonial analysis of confluence. I also outline this study’s focus on historical continuities by attending to the temporal (that which is dynamic and changing and that which is continuous yet carried through time). I outline what is drawn on (partially) from Foucauldian genealogical analysis and how this method departs from this. I also illustrate how the method used in this study attends to material continuities (how projects of nation building relied/rely on eugenic and racial knowledge formations and disciplinary processes and laws) through an attention to processes that discursively frame people to construct, legitimize, and authorize violence. By acknowledging that all our interpretive and discursive structures are subject to historical influence, it is also pertinent that we disclose the horizon of these interpretations. As Hans-Georg Gadamer made very clear in his magnum opus Truth and Method , every person has a historically effected consciousness, making claims to objective knowledge impossible. Through our concurrence with this position, we can appreciate that all vantage points are partial, contingent, and subject to representation and interpretation. The horizon of interpretation in this study is committedly focused with an attention to the levels of analyses and contribution provided by postcolonial theory.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".