Lifelong learning as a chameleonic concept and versatile practice: Y2K perspectives and trends
Bibliographic record
Abstract
This essay focuses on contemporary lifelong‐learning discourse as it was reflected in deliberations during three events held in Australia, Canada and the UK during 2000–01. Through the dialogical lenses of these Y2K events that brought together an array of international participants, it examines lifelong learning as a chameleonic concept and versatile practice in education and culture. It considers how participants at the three events framed lifelong learning’s parameters and complexities as they discussed perspectives and trends shaping lifelong‐learning discourse, policy‐making and practice. In doing so, three pervasive Y2K‐event themes are discussed: (a) lifelong learning encompasses instrumental, social and cultural education; (b) lifelong learning involves mediation of public and private responsibilities; and (c) lifelong learning occupies a precarious and paradoxical position in a world that desires to position it as a permanent global necessity. The essay concludes with a perspective on lifelong learning as a critical practice in a world where culture as knowledge and culture as community vie for space. It locates this practice in inclusive, holistic terms, suggesting that a critical practice of lifelong learning is guided by a key aim: to help persons become responsive and responsible citizen learners and workers who are able to think, speak and act in life, learning and work situations.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.055 |
| Scholarly communication | 0.026 | 0.016 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".