Bibliographic record
Abstract
Between July 13 and July 15, 2022, a group of about 60 Filipinx Canadian scholars, artists, organisers, and community members gathered at York University, located in Toronto,for a transdisciplinary workshop called FilipiNEXT. Participants came from across Canada and the United States, not only from major urban centres of Toronto and Vancouverbut also from smaller cities and towns such as Halifax (Nova Scotia), Hazelton (British Columbia [BC]), Winnipeg (Manitoba), and Calgary (Alberta) as well as Honolulu, Hawaii, and Ithaca, New York. The diverse demographic geographies that characterised the workshop were thus markedly different from previous Filipinx Canadian anthologies and gatherings, which tended towards participants from Southern Ontario and Greater Vancouver. As organisers, we wanted the workshop to mirror the geographical distribution of Filipinx academics in Canada. Along with traditional academic presentations, panels, and discussions, the gathering featured the work of visual and performing artists, a graduate student-focused workshop, as well as informal modes of gathering—chikahan, kwentuhan and tsismisan— over food and refreshments. Among other things, those of us who gathered at FilipiNEXT had the opportunity to bear witness to and learn from the current state of scholarship about Filipinx lives, cultures, and communities in Canada; discuss what it means to be Filipinx folks navigating institutions such as academia, art worlds, and organising communities; and articulate our desires and visions for the future of Filipinx studies in Canada.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.040 | 0.004 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.099 | 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".