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Record W4410143004 · doi:10.5070/ln43265253

Setting the Scene: An Introduction to FilipiNEXT

2025· article· en· W4410143004 on OpenAlexaboutno aff
John Paul Catungal

Bibliographic record

VenueAlon Journal for Filipinx American and Diasporic Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.774
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.401
Teacher spread0.366 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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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