Lifelong learning in the digital age: A content analysis of recent research on participation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents results from a cross-disciplinary content analysis of 185 recent research articles, published between 2008 and 2013. These papers examined factors affecting adult participation in lifelong learning, based on the availability and use of Internet-based and face-to-face modes of learning. Articles were written by scholars from 39 countries, including the European Union (EU), United States (U.S.), Canada, Australia, and, to a lesser extent, from developing and newly industrialized countries, such as Mexico, Brazil, China, and Taiwan. Despite widespread assumptions as to online learning’s potential and promise, articles focused on traditional face-to-face learning and training modes more than Internet-based modes. Seven thematic research areas were identified from the dataset: four major and three emerging themes. Key findings from 40 studies about the adult participation in learning in the workplace and community-based programs are highlighted. These papers present broad and deep investigations about diverse groups of lifelong learners previously unstudied, while equity issues pertaining to access and availability of training and learning opportunities are addressed. Directions for future research are identified and discussed.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it