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Record W7084245389

The Tragedy of Komagata Maru (The Sikh Challenge to Canadian Immigration Politics:1897-1920)

2025· review· en· W7084245389 on OpenAlexaboutno aff

Bibliographic record

VenuePhilPapers (PhilPapers Foundation) · 2025
Typereview
Languageen
FieldEnergy
TopicGlobal Energy Security and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsTragedy (event)PoliticsImmigrationDual (grammatical number)DeportationCenter (category theory)
DOInot available

Abstract

fetched live from OpenAlex

Dr. Puran Singh, the author of The Tragedy of Komagata Maru, is a distinguished writer and journalist based in Vancouver, B.C., since 1976. Dr. Puran Singh’s expertise is exceptionally well-suited to the topic of The Tragedy of Komagata Maru due to his extensive academic background, professional experience, and personal involvement in the Sikh community. His dual master’s degrees in history and political science, along with his Ph.D., provide him with a deep understanding of historical events and political dynamics, both of which are crucial for analyzing the Komagata Maru incident and its implications.His professional experience as a Lecturer in Political Science has equipped him with the skills to critically evaluate and explain complex sociopolitical issues. Furthermore, his long tenure as a Library Assistant at the Asian Library and Library Processing Center at the University of British Columbia demonstrates his proficiency in research and access to a wealth of academic resources, which is essential for conducting thorough historical research.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.161
Threshold uncertainty score0.323

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.023
GPT teacher head0.299
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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