Bibliographic record
Abstract
Abstract The trouble with texts, especially if they are ancient and sacred, is that they can be summoned and assigned meanings to prove or legitimize any cause, theory, or perspective. Interpretative history is littered with such examples. When European colonialism was at its peak, biblical texts were taken out of context to prove biblical sanction for such a venture. Let me extract a gem from colonial history. When, for instance, Britain gradually gained power and expanded its territorial control in the north, south, east, and west, such an expansion was seen as the fulfilment of biblical expectation. New Foundland, Britain’s first colony in the West, was acquired in 1583. Territorial gains were made in the East in the sixteenth century. The most northerly of Britain’s possessions, Canada, was added in the eighteenth century and its southern dominions—Australia, New Zealand, and South Africa—were colonized in the early nineteenth century. Such a territorial acquisition was perceived by Bernard Bateson as Britain fulfilling the prediction of Gen. 28: 14: ‘Thou shalt spread abroad to the west, and to the east, and to the north, and to the south’ (Bateson 1947: 63). What this volume attempts to do is to go beyond such a facile reading of texts and to look deeply at the way colonialism interconnects with texts and interpretation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.495 | 0.290 |
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".