A Content Analysis of the Journal of Distance Education 1986-2001
Bibliographic record
Abstract
The Journal of Distance Education (JDE) (1986-2000) was subjected to a content analysis (235 items) that focused on item type, topic, research method, and biographical information about first authors. A coding scheme was created for these categories. The distribution was balanced between male and female authors, national and international viewpoints, and a broad range of topics. In the subset of items coded as empirical studies, the highest frequencies of (a) item topic were foundations of distance education and technology/medium; (b) language of presentation: English; c) institutional affiliation of author: higher education; (d) type of data collected and analyzed: qualitative. The Journal of Distance Education (JDE) (1986-2000) a été assujetti à une analyse de contenu (235 items) qui s’est concentrée sur le type de l’item, le sujet, la méthode de recherche et l’information bibliographique des premiers auteurs. Un système de codification a été créé pour ces catégories. La répartition a été équilibrée entre les auteurs hommes et femmes, les points de vue nationaux et internationaux et une vaste étendue de sujets. Dans le sous-ensemble des items codifiés comme étant des études empiriques, la plus haute fréquence était pour (a) sujet de l’item : les bases de l’éducation à distance et technologie/média; (b) langue de présentation : anglais;© affiliation institutionnelle de l’auteur : enseignement supérieur; (d) type de données recueillies et analysées : qualitatif.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".