Letâ s Talk About the NOC: An Ethnography of Classification
Bibliographic record
Abstract
This ethnographic study describes the background of standard national occupational classification in Canada and analyses its use within an organization that supports employment initiatives for newcomers to Canada. Original in its approach, the study responds to questions about the social role of a classification system through ethnographic description of who is involved in working with occupational classification. Fieldwork methods included gathering instantiations of the National Occupational Classification (NOC) and its antecedent occupational classification systems, participant observation, interviews and documentary research. Analytical techniques included writing and visual analysis in relation to conceptions of knowledge organization and information drawn from the disciplines of Library and Information Science. The topic and approach are significant towards extending the range of knowledge organization systems analyzed in library and information science (LIS) disciplines, and bridging the interests of knowledge organization research and information practices and social epistemology. Findings demonstrate the existence of complex relations among organizational practices such as producing algorithmic matches between occupational categories, tracking and communicating quality measures to stakeholders, and informing the design of new organizational systems. Producing matches, measuring quality, and informing design in turn exist in relation to both broader social organization systems such as immigration and the economy and narrower engagements of everyday life. The mediating role played by standardized occupational classification exists orthogonal to the everyday life of individuals and socio-economic institutions and is brought into view through description and analysis based in local organizational settings. The discussion turns toward a deeper analysis of the specific role of the NOC in this organizational setting by putting into relation with concepts of knowledge organization and library and information science research. Methodological techniques are also considered in greater detail in the discussion. Significantly this study combines ethnographic techniques for data gathering with analytical approaches drawn from the domain of knowledge organization. It will serve as a resource for future investigations of occupational classification in social practices of interest to Library and Information Science.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.020 | 0.018 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".