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Record W7062732819

Wounds Water the Buds. An Iranian Immigrates to Canada; An Illustrated Autobiographical Short Story

2022· dissertation· en· W7062732819 on OpenAlexaboutno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2022
Typedissertation
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Measurements
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeValue (mathematics)Psychological resiliencePeriod (music)Visual methodsDigital artVisual languageOrder (exchange)
DOInot available

Abstract

fetched live from OpenAlex

In this study, I explore the reasons for migration, my development as an art educator and artist, and resilience among adult immigrants. As a visual storyteller, I present a graphic novel as a means to explore the experiences of immigrant art educators from a sociopolitical, economic, and cultural perspective. For this investigation, I employ art-based research and a research-creation approach that involves collecting and visualizing my experiences in a digital notebook and incorporating these images into adults’ autobiographical graphic novel/ comics. \n \nThis study has implications for art education as it constitutes a meaningful attempt to understand the pedagogical turning points in the value of graphic novels. Moreover, the study is also significant for demonstrating that earlier experiences are a crucial area of inquiry in art instruction. I developed my personal history into a graphic novel in order to portray my experience in a visual form.

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)0.001
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: none
Teacher disagreement score0.277
Threshold uncertainty score0.558

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0250.007
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.021
GPT teacher head0.248
Teacher spread0.227 · 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
Published2022
Admission routes1
Has abstractyes

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