MétaCan
Menu
Back to cohort
Record W4393316087 · doi:10.1177/1086296x241244682

Temporal Seeing as Visual Literacy

2024· article· en· W4393316087 on OpenAlexaff
Roger Saul, Julianne Gerbrandt, Casey Burkholder

Bibliographic record

VenueJournal of Literacy Research · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPsychologyLiteracyVisual literacyReading (process)Mathematics educationCognitive psychologyPedagogyLinguistics

Abstract

fetched live from OpenAlex

Temporal seeing is a mode of visual perception that interrupts the spatial bias we bring to visual literacy practices. Although an image only captures one moment in time, there are multiple spatioanalytical tools we can use to consider any image. Spatial literacy, which is the practice of analyzing objects through their properties in space, tends to be the default analytical mode for making sense of imagery. For people to bring a commensurate temporal richness to their articulated visual readings, we first highlight the perspectival richness of time and temporality. We next present five precepts that can guide enriched temporal seeing: contextual histories; relational chronologies; internal rhymicity; desequenced and resequenced narrative; and critique and meaning-making. Finally, we suggest that temporal seeing holds a series of educative possibilities for expanding the interpretive frames and perceptual apparatuses of literacy researchers and practitioners.

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)0.003
metaresearch head score (Gemma)0.010
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: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.039
Scholarly communication0.0100.013
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.095
GPT teacher head0.470
Teacher spread0.374 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Literacy ResearchSame topicLiteracy, Media, and EducationFrench-language works237,207