Bibliographic record
Abstract
This dissertation is an innovative exploration of the intersections between participatory archiving and augmented reality (AR), rooted in the necessity to engage more fully with Toronto’s diverse and historical cultural heritage. Foregrounded as research-creation, this work describes, theorizes, and disseminates an AR counter-archive— me-deÌ rive: toronto. Mobilizing power away from institutional archives and into the hands of the public, me-deÌ rive: toronto uses the power of locative media in order to provide records of Toronto’s diverse past and present in-situ. A portmanteau of the word ‘mediation’ and the Situationist’s notion of the deÌ rive, the app produces a new paradigm to the archive— one that is simultaneously participatory and techno-informed. With every found photograph and submission alike the project multiples both in volume and vigor to combat the archival injustices that fail to narrativize Canadian immigrant identities. This counter-archive reclaims space through a participatory visual account of history, allowing for a different kind of knowledge—one that is techno-embodied—to emerge and be embraced. As the scope of the records and overall scale of the project amplifies, it engages a layer of complementary principles— the accrual of diverse narratives, the opposition of pre-prescribed history by governing institutions, and the dynamism to employ technology to engage critically with heritage records beyond the confinements of exclusive cultural spaces. Through historical research, precedent scanning, insights into cultural production, participatory and mobile app observation and research-creation projects, counter-archival projects demand that institutions become more inclusive, not only in collection practices, but in dissemination and access practices as well. My research addresses the gaps and omissions that exist in our institutional archives, while making explicit what is needed to make a more holistic, comprehensive archive of Toronto’s diverse narratives.Â
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.036 | 0.023 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.047 | 0.006 |
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".