Seeking balance: Professional development needs of tenured information science faculty
Bibliographic record
Abstract
ABSTRACT There is minimal, mainly anecdotal, evidence of discourse about the professional development of information science (IS) academics. In an effort to initiate discussion on professional development and encourage information sharing among IS faculty and administrators, we organized a panel at the 2016 Annual Meeting of ASIS&T (Lopatovska et al., ). The panel brought attention to the professional development resources and strategies available to mid‐career faculty and uncovered the need to continue the discussion. This proposed interactive panel aims to identify tenured IS faculty members' needs related to their scholarly, teaching and service pursuits, explore professional wellbeing and work climate, share best practices and brainstorm potential solutions for some of the identified issues. The issues and solutions uncovered during the panel discussion will be shared with the broader community with the hope that recommendations can be adopted and acted upon by individuals, academic institutions and professional associations. The panel will be of interest to faculty, doctoral students and academic administrators.
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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".