Photography as a legitimate technique for domain analysis in Knowledge Organization
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it