Tensions Between Language and Discourse in North American Knowledge Organization
Bibliographic record
Abstract
This paper uses Paul Ricoeur's distinction between language and discourse to help define a North American research agenda in knowledge organization. Ricoeur's concept of discourse as a set of utterances, defined within multiple disciplines and domains, and reducible, not to the word but to the sentence, provides three useful tools for defining our research. First, it enables us to recognize the important contribution of numerous studies that focus on acts of organization, rather than on standards or tools of organization. Second, it gives us a harmonious paradigm that helps us reconcile the competing demands of interoperability, based on widely-used tools and techniques of library science, and domain integrity, based on user warrant and an understanding of local context. Finally, it resonates with the current economic, political and social climate in which our information systems work, particularly the competing calls for protectionism and globalization.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.019 | 0.042 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".