Book Review: [Ab]Using Power: the Canadian Experience, by Susan C. Boyd, Dorothy E. Chunn and Robert Menzies (eds)
Bibliographic record
Abstract
The "average" Canadian is more likely to suffer at the hands of government, elected and appointed officials, business organizations, professionals or white-, blue-and khaki-collared criminals than from all the street thugs, youth gangs, home invaders, illegal (im)migrants, pot growers and squeegee kids that our society can produce.3 Through its many concrete and detailed examples, [Ab]Using Power: The Canadian Experience functions as a useful primary text, documenting the misuses of power and the malfeasance of the powerful within a single collection of essays.Its interdisciplinary quality also makes it an important contribution to our theoretical understanding of power and power relations.The editors set out to explore instances of illegitimate, unethical, dangerous, and often harmful conduct committed by the state and its political elites, private corporations, professionals, as well as other authorities wrongfully wielding their power.The editors seek to redirect our focus from street crime onto the abuses of power committed by persons in authority.In doing so, they challenge the hegemony that criminalizes the powerless and crystallizes a collective fear of, and anxiety about, the disenfranchised members of society.The editors neither minimize the 1[ [AbjUsing Power].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".