Photography as a legitimate technique for domain analysis in Knowledge Organization
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".