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Record W2783932429 · doi:10.3138/ctr.173.005

For This Land

2018· article· en· W2783932429 on OpenAlexvenueno aff
Jackson bears, Janet L. Rogers

Bibliographic record

VenueCanadian Theatre Review · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicArtistic and Creative Research
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeStorytellingOpenness to experienceIndigenousEmbodied cognitionAestheticsSociologyMedia studiesVisual artsPsychologyArtSocial psychologyLiteratureComputer science

Abstract

fetched live from OpenAlex

For This Land provides an overview of the philosophy, concepts, practice, and inspiration adopted by one Indigenous media team. The 2Ro Media team has discovered, through their creative voice, a means to carry themselves back to their community from where they were not raised, where they felt a part of, and where they long to return. The article speaks of ways the team is asking permission to return by placing themselves on the land of their territories and engaging in new conversations with the land through interactive embodied storytelling. The narratives produced are both personal and universal. The artists recount, with brave openness, the vulnerability of their experience. The article presents an opportunity for the reader to place themselves inside their story to explore relatable narratives in their own lives. Through the story offerings embedded in the article, there exists potential for common experience to be explored thus creating spaces of compassionate and deeper relationships with each other.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.863
Threshold uncertainty score0.756

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.003
Scholarly communication0.0050.004
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.2260.060

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 head0.071
GPT teacher head0.312
Teacher spread0.241 · 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
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

Citations2
Published2018
Admission routes1
Has abstractyes

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