Bibliographic record
Abstract
Various partnerships between stakeholders drive the coordination and implementation of RVA. Cooperation with industrial organisations and the private sector can be advantageous; however, there are issues that the capacities promoted will often be narrowly focused around market sector skills. Public authorities have an important role to play through a broad range of activities including the development of goal-oriented public policies on RVA, the identification of key sectors in the economy requiring sustained efforts to build human capital; as well as collaborative work among different ministries etc. The shared responsibility or “social partnership” model based on close cooperation between the government, social partners and other societal stakeholders is becoming an inevitable feature of the development and implementation of RVA policies and practice. A unique feature of stakeholder involvement in the adult learning sector has been the engagement of adult educators. Canada, the US and UK have promoted RVA as a social movement for adult participation. In developing countries, with vast decentralised systems of non-formal and adult education, NGOs and voluntary agencies, as well as local and district governments are active in imparting non-formal education to socio-economically weaker sections. With a broad range of interests at stake, many objectives formulated in RVA policy respond to economic goals. Other objectives relate more closely to education and training system and qualifications reforms. In all countries, however, promoting and facilitating the integration and empowerment of marginalised social groups and individuals (uneducated and unemployed) and strengthening the motivation for lifelong learning are highly important policy objectives. Despite the various options for recognition according to the interests at stake, the chapter argues that all actors must be responsible for rendering competences visible and documenting them and enabling the process towards a qualification, diploma or certificate in cooperation with national authorities, and without neglecting coherence, transparency and quality. Recognition policies therefore need to reflect directly the level of cooperation and consensus-building between education, employment, economic and civil society actors. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.031 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".