Book review of "Maritime Heritage in Crisis: Indigenous landscapes and global ecological breakdown" by Richard M. Hutchings. New York, Routledge, 2017. ISBN: 978-1-62958-8
Bibliographic record
Abstract
[Extract] Reading the preface of Richard M. Hutching's provocative Maritime Heritage in Crisis, I found myself agreeing with the prominent archaeology professor who argued that archaeologies' colonialist agenda, driven by capitalism and producing elitist, racist and pro-development narratives, is "old news" (xii–xiii). However, by the conclusion of the book, Hutchings had convinced me that this "head in the sand" attitude is no longer sustainable. Archaeologists and resource managers model themselves as experts", "saviors" and "specialists", yet hide behind these roles like the pianists on RMS Titanic – part of the problem yet blind to the fact. For decades, archaeology and cultural resource management (CRM) have been complicit in the destruction of Indigenous heritage in colonial states like Canada and America – the settings for this volume. Yet, as argued by Hutchings in his frank, first person voice, this destruction is as inherent today, to the point that he describes it as a type of "violence" (96). Through controlling heritage, archaeology and CRM dominate the production of knowledge about Indigenous pasts, and so control these people (101).
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.066 | 0.027 |
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".