How deep is the difference? Parsing Difference in the Context of Canadian Diversity Through Documents and Documentary: A thesis paper to support the multimedia installation Illegal Entries
Bibliographic record
Abstract
This paper follows the blurriness of the concept of difference as it relates to the Canadian idea of diversity, and its manifestation in governmental forms and official documents. It highlights the paradoxical (inconsistent) function of such documents in addressing diversity, individuation and change. This research was informed by the concept of difference, defined (interpreted) by Manuel DeLanda, and is expressed in a multimedia installation titled Illegal Entries, which reconfigures the Canadian Passport Application form (PPTC 153-154) as a three-dimensional space. This installation shows viewers that this official document, which operates to legitimize state power over citizens’ bodies, is designed to undermine the concept of difference, which is at the core of individuation and is the backbone of diversity and multiculturalism in Canada. This paper provides an account of how a document is transformed into a documentary. The installation creates a bilingual, audio-visual conundrum that consists of institutional texts, commands and warnings. This is juxtaposed with animated graphics, icons and shapes that appear in the document in addition to the image of the passport ID photograph.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.022 | 0.028 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".