Bibliographic record
Abstract
Revisiting memories -both lived and cinematic -from my childhood and teenage years, an era during which Hong Kong went through some of its most tumultuous trials and tribulations, was a painful experience.Furthermore, to analyse how it feels to be a Hong Konger and why it is so difficult to articulate those feelings required a level of self-honesty that was at times emotionally draining.I am therefore tremendously grateful for many people who walked me through this extraordinary journey.'Extraterritoriality' as a concept was inspired by my many conversations with my friend and mentor Thomas LaMarre, with whom I had the privilege to work at McGill University from 2010 to 2012.Its intricacies and complexities have been enriched over the years under the generous guidance of Thomas Elsaesser, the unconditional support of Dudley Andrew and Haun Saussy, and the patience and encouragement from Chris Berry.Every word of this book, in truth, is indebted to the intellectual companionship of my friend George Crosthwait.I feel incredibly honoured that in the last eight years, many filmmakers, critics and scholars whose works I discuss in this book have put every confidence in my project.Some I have met in chance encounters, whose works and words have left indelible traces on these pages.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.378 | 0.259 |
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".