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Record W3156447324 · doi:10.15402/esj.v6i2.69984

PhoneMe Poetry: Mapping Community in the Digital Age

2021· article· en· W3156447324 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2021
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsUniversity of British ColumbiaYork University
Fundersnot available
KeywordsPoetryNeighbourhood (mathematics)UploadRepresentation (politics)Space (punctuation)PhoneDigital mediaSocial mediaSociologyVisual artsMedia studiesMultimediaWorld Wide WebComputer scienceArtLiteratureLinguisticsPolitical science

Abstract

fetched live from OpenAlex

In this paper we explore how place-based poetry mediated online enabled community self-representation. Located in the urban core of a large cosmopolitan Canadian city, the PhoneMe project brought together academic researchers and community members into a collaborative educational creative space. Community members created poems about specific places within their neighbourhood, dialed a designated phone number, and recorded the poem by leaving a voice message. Upon receiving the message, the academic team geotagged it on an interactive map, uploaded the poem’s text, featured a Google Streetview image of the location, and shared the post via social media. As the result, a new vision for this distinctive physical space emerged and reached the wider audiences via engagement with the poetic digital media.

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.

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)0.834
metaresearch head score (Gemma)0.736
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.853
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.8340.736
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.6000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.853
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 head0.307
GPT teacher head0.466
Teacher spread0.159 · 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