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Record W7125225448 · doi:10.14288/bcs.no227.200605

Mahal, Kapwa, Dasal

2025· article· en· W7125225448 on OpenAlexaff
Teilhard Paradela, Dada Docot, Karla Lenina Comanda

Bibliographic record

VenueOpen Collections · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPhilippine History and Culture
Canadian institutionsWomen and Gender Equality Canada
Fundersnot available
KeywordsIndigenousExhibitionPoliticsContext (archaeology)The artsColonialism

Abstract

fetched live from OpenAlex

This article presents an intellectual history of the University of British Columbia Philippine Studies Series (PSS), a network of scholars and students working on the Philippines and the Filipino/a/x diaspora, that sought to build community, foster dialogue, and establish solidarities through lectures, art exhibits, and forums from 2011 to 2021. Drawing from the critical conversations among three authors, the article explores how the PSS was shaped by both its scholarly commitments to the Philippines and its diaspora, and its ethical and political orientation toward doing this work on unceded Indigenous land. The article traces the development of the PSS’s thinking on what it means to do academic, advocatory, and artistic work within a settler colonial context through three major PSS projects: the three-part exhibition series Mahal; Kapwa: Sensing Ourselves in One Another, a Philippine Indigenous arts festival; and Padasal Para Sa Mga Patay (Prayer for the Departed), a performance reimagining the Catholic prayer-novena. By situating the PSS within broader debates on migration, decolonization, and the politics of knowledge production, the article reflects on the risks, contradictions, and possibilities of building Filipino/a/x studies in British Columbia as an ongoing decolonial practice of accountability, relationship, and care.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.115
Threshold uncertainty score0.229

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0160.007
Scholarly communication0.0070.004
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0210.003

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 head0.023
GPT teacher head0.340
Teacher spread0.317 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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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