Steps for Creating a Specialized Corpus and Developing an Annotated Frequency-Based Vocabulary List
Bibliographic record
Abstract
This article provides introductory, step-by-step explanations of how to make a specialized corpus and an annotated frequency-based vocabulary list. One of my objectives is to help teachers, instructors, program administrators, and graduate students with little experience in this field be able to do so using free resources. Instructions are first given on how to create a specialized corpus. The steps involved in developing an annotated frequency-based vocabulary list focusing on the specific word usage in that corpus will then be explained. The examples are drawn from a project developed in an English for Academic Purposes Nursing Foundations Program at a university in the Middle East. Finally, a brief description of how these vocabulary lists were used in the classroom is given. It is hoped that the explanations provided will serve to open the door to the eld of corpus linguistics. Cet article présente des explications, étape par étape, visant la création d’un corpus spécialisé et d’un lexique annoté et basé sur la fréquence. Un de mes objectifs consiste à aider les enseignants, les administrateurs de programme et les étudiants aux études supérieures avec peu d’expérience dans ce domaine à réussir ce projet en utilisant des ressources gratuites. D’abord, des directives expliquent la création d’un corpus spécialisé. Ensuite, sont présentées les étapes du développement d’un lexique visant le corpus, annoté et basé sur la fréquence. Les exemples sont tirés d’un projet développé dans une université du Moyen-Orient pour un cours d’an- glais académique dans un programme de fondements de la pratique infirmière. En dernier lieu, je présente une courte description de l’emploi en classe de ces listes de vocabulaire. J’espère que ces explications ouvriront la porte au domaine de la linguistique de corpus.
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.
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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.015 |
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".