Bibliographic record
Abstract
This paper presents preliminary analyses of the coverage that the ICE-CORE word list provides of a diverse range of English language texts. The objective of this investigation was to further test the limits of this word list. To date, the ICE-CORE word list has been used to profile corpora of spoken and written discourse of more than 25 international English varieties. Findings consistently indicate that very few words account for most of the language produced in local and global interactions. It is based on these and similar findings that it has been proposed that the ICE-CORE word list represents the lexical core of the English language. This investigation aimed at seeking the limits of this proposal by examining widely different corpora which contain texts prepared for very different purposes and audiences. 1.0 Introduction The ICE-CORE word list is a modern embodiment of a well-established approach to vocabulary learning (Nation, 2001). It contains 1,206 words that are used extensively by English speakers around the world in both speech and writing. Briefly put, the items on this word list were originally identified by comparing corpora representing seven Inner and Outer circle English varieties, specifically, from Canada, East Africa, Hong Kong, India, Jamaica, the Philippines, and Singapore. The words that occurred with similar high frequency in all of these varieties were included on the ICE-CORE word list. Since its inception, the ICE-CORE word list has been tested on a variety of corpora (Gilner, 2008; Gilner & Morales, 2011; Gilner, Morales, & Shiobara, 2012; Gilner, 2013). The initial work was done against the ICE corpus from which it originates. Subsequent analyses were conducted against the 26 English varieties collection (Gilner et al., 2012) and the VOICE corpus (Gilner, 2013). All work to date has shown that the ICE-CORE wordlist accounts for 75%-90% of the lexical choices made by worldwide English speakers in colingual as well as international settings
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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 | 0.007 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".