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Record W2804447071 · doi:10.5771/9783956504211-392

Photography as a legitimate technique for domain analysis in Knowledge Organization

2018· book-chapter· en· W2804447071 on OpenAlexaffabout
Eva Hourihan Jansen

Bibliographic record

VenueErgon Verlag eBooks · 2018
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiomedical Text Mining and Ontologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhotographyDomain (mathematical analysis)Domain knowledgeComputer scienceKnowledge managementVisual artsArtMathematics

Abstract

fetched live from OpenAlex

This paper presents findings from a study of occupational classification in the context of employment support for newcomer professionals. The context of this investigation is Canada’s standard occupational classification, a member of a genre of state sponsored statistical classification systems used in labour markets around the world. The study was set in a not-for-profit organization that supports the provision of services to newcomer professionals within a network of community service providers and employers in the local labour market. As a KO system that is constructed through consultation with a broad group (Howarth & Hourihan 2014), it therefore demands a collectivist approach like domain analysis. As a collectivist approach that recognizes experience is shaped by social and cultural communication, domain analysis takes the unit of analysis beyond the individual to the group level and looks toward characteristics of the environment (Hartel 2003).  Hence, in KO, among the techniques for visualizing domains that appear most often in the literature citation analysis is considered a valid form of visualization in KO (Smiraglia 2015).  Visualization offers the advantage of providing a graphic overview of a domain (Smiraglia 2015, p 95). Recently, the Sixth North American Symposium on Knowledge Organization (NASKO 2017) contemplated visualizing knowledge, knowledge organization, and knowledge organization systems. Clearly, different forms of visualization can lead to navigational maps and some recent examples include citation analysis (Smiraglia 2017), cladistic visualizations (Campbell & Mayhew 2017), knowledge graphs (Zhao, Ma & Xia 2017) meta-theoretical visualizations (Araujo, Tennis & Guimares 2017), along with node link diagrams and cover images (Hook & Gantchev 2017). This paper describes data collection and analysis techniques to position photography and photographs as another useful method towards accomplishing knowledge organization (KO) research.

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)0.008
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0080.006
Science and technology studies0.0040.006
Scholarly communication0.0070.007
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0160.002

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 head0.011
GPT teacher head0.264
Teacher spread0.253 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2018
Admission routes2
Has abstractyes

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