Studies in settler colonialism : politics, identity and culture
Bibliographic record
Abstract
Introduction F.Bateman & L.Pilkington 'An Unknown and Feeble Body': How Settler Colonialism Was Theorised in the Nineteenth Century T.Foley Spenser, Purchas, and the Poetics of Colonial Settlement D.Carey 'Dycheyng and Hegeying': The Material Culture of the Tudor Plantations in Ireland J.P.Montano A Settled Question?: Charles, Lord Cornwallis, the Loss of America and the Mind of Empire D.Dix International Anti-Colonialism: The Fenian Invasions of Canada R.Young Indirect Rule in Australia: A Case Study in Settler Colonial Difference B.Silverstein (En)gendering Faith?: Love, Marriage and the Evangelical Mission on the Settler-Colonial Frontier C.McLisky 'Wanted! A Real White Australia': The Women's Movement, Whiteness and the Settler-colonial Project, 1900-1940 J.Carey From the Indigenous to the Indigent: Homelessness and Settler Colonialism in Hawai'i L.E.Lyons Searching for the 'C' word: Museums, Art Galleries, and Settler Colonialism in Hawai'i K.K.Kosasa A Dream Deterred: Palestine from Total War to Total Peace J.Collins Displaced Nations: Israeli Settlers and Palestinian Refugees S.D.Hassan Telling the End of the Settler Colonial Story L.Veracini JM Coetzee and the Idea of Africa D.Attwell Zionism Then and Now S.Makdisi Where We Belong: South Africa as Settler Colony and the Calibration of African and Afrikaner Indigeneity E.Boehmer Race and the Trace of History P.Wolfe
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".