Bibliographic record
Abstract
Although key stakeholders in blended tertiary environments (BTEs)fulfil an extraordinary role in higher education, significant gaps in knowledge about their identities may be impeding the provision of stakeholder support, limiting their ability to promote effective learning and teaching. As online growth intensifies, it is critical that tertiary education institutes (TEIs) address these gaps in knowledge by developing their understandings of key stakeholder identities. This paper re-evaluates the identity of key stakeholders in BTEs, and describes their contributions. Through qualitatively designed semi-structured interviews with 13 blended learning experts from New Zealand, Australia and Canada, and a 5-step analysis of data, it verified and proposed a current list of key stakeholders in BTEs. This included teachers, senior management staff, students, technical support staff, educational support staff, the institute, other support staff, government bodies, technology infrastructure providers, communities, and the public. Some were considered to be among those who contributed most significantly to BTE success. As learning spaces evolve and technology usage accelerates, the outcomes from this research will provide a basis from which TEIs can develop new understandings about their key stakeholders, to help them deliver informed, relevant, and meaningful support.
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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".