The Heart of the Matter: Methodological Challenges in Developing a Contemporary Reading Programme for Monolingual Lexicography, from the Perspective of the Dictionary Unit for South African English*
Bibliographic record
Abstract
This article argues the importance of the reading programme as the pivotal issue in the lexicographic process. It is essentially a practical article which outlines strategies for developing and implementing a reading programme for monolingual lexicography. The arguments are informed by theory, together with an examination of the data-collection procedures followed by the Dictionary Unit for South African English (DSAE) and a survey of current practice in major English dictionary units around the world, namely the Oxford English Dictionary, the Canadian Oxford Dictionary, the Australian National Dictionary Centre and the New Zealand Dictionary Centre. The reading programme for the DSAE is first defined and contextualized within its mission statement. The article then explores the challenges inherent in sampling contemporary written and spoken English in the South African multilingual context. It is intended to inform the DSAE's intake policy, in terms of the following critical issues: — the definition of South African English, — the monitoring and selection of print, oral and electronic sources, — the excerpting of citations and relevant bibliographic information, and — the recruiting and training of readers. These interlinked aspects of the reading programme have crucial implications for the quality and authority of the monolingual dictionary on historical principles. Keywords: reading programme, monolingual dictionary, historical principles, contemporary, intake, citations, strategies
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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 | 0.297 | 0.390 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.010 | 0.040 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".