Bibliographic record
Abstract
Purpose Small and specialist inter‐disciplinary conferences, particularly those relating to technology enhanced learning such as International Conference on Information and Communications Technology in Education, provide valuable opportunities for academics and academic‐related/professional staff to report upon their research and development activities, including their insights into teaching practice. However, the existence of such conferences is now under threat due to a global shift towards quantitative research assessment exercises, which favour bibliometrics, such as citation counts and impact factors, over peer review. The purpose of this paper is to contextualise the discussion by describing the nascent qualitative research assessment in Australia and its implications for small conferences. It also aims to present heuristic strategies to ensure that publications are recognised by quantitative research assessment exercises. Design/methodology/approach The authors draw on a wide literature base as well as their experience as academics, conference organizers, professional developers, and researchers to describe the changes to the culture of research assessment and research management and their observed implications for small and specialist inter‐disciplinary conferences. Findings Conference organizers and scientific committees should consider several strategies to maximise bibliometric impact of conference papers. These strategies include: transparency in reviewing processes; building alliances with peer‐reviewed journals; considering boutique “by invitation” conference formats; and publishing papers which are indexed and standards based. The authors also point out that small and specialist conferences should leverage their communities of practice to facilitate publication and research opportunities and thereby increase the tangible benefits of participation. Originality/value This paper is valuable to conference organizers and participants who are adjusting to a culture of bibliometrics. This paper highlights key issues as well as suggests strategies to improve impact values.
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 | 0.145 | 0.410 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".