“Put your mask on first before helping others”: Faculty members as a neglected population during Covid
Bibliographic record
Abstract
In congruence with the conference theme, “Crafting a Resilient Future: Leadership, Education, & Inspiration”, our panel seeks to address the ways in which faculty members in LIS/IS programs have contended with the various changes and challenges stemming from the global pandemic of COVID-19 as well as broader –and related- trends reshaping the academic landscape. While most of the attention in the literature has been geared toward student engagement and learning online as a means of addressing students’ academic success and wellbeing (Rapanta et al., 2020; Katz et al., 2021), there has been disproportionally much less attention geared at teaching and research faculty members. Despite being the backbone of our educational programs and schools, and often the main reasons why students select to enter our field (Dali & Caidi, 2016), faculty members’ needs and the challenges they are facing have been largely ignored (El Masri & Sabzalieva, 2020; Gabster et al., 2020). In this panel, we seek to critically center our discussion on this key constituency, and question (disrupt, even) the notion of faculty resilience. Indeed, making use of the resilience trope sheds light partially on faculty members’ well-being, but it also contributes to masking the many inadequacies and failures at the organizational and systemic level, particularly around policies and practices dealing with the curriculum, workload, representation, accommodations, academic freedom, resource allocation, justice and dignity to name just a few. There is a much-needed engagement that needs to take place around these issues in LIS education if we are truly honest about resilience and sustainability.\n\nOur international panelists present a cross-section of faculty members who bring their varied experiences in teaching and research in the LIS field to the discussion. Together, they represent tenure and tenure-track faculty, and administrators across three countries (USA, Canada, New Zealand). The panelists, all LIS educators and professionals, will base their engagement on the following themes/questions:\n• What efforts are LIS programs making to address the challenges faced by faculty members to ensure not only the sustainability of the educational program but also a dignified and fair treatment of faculty members? \n• What are possible scenarios for a post-COVID future of LIS education, and how can faculty members be best supported and inspired to achieve resilience for a sustainable future?\n\nThe speakers will tackle different angles to address these questions. After a short lightning talk (7-8 minutes), a discussion among panelists will ensue as well as engagement with the attendees through a Q/A. Some of the topics discussed include academic freedom, disability and neurodiversity, BIPOC faculty, emergency preparedness, and information cultures in Higher Education. \n\n\nOur format will be an interactive panel discussion that focuses on lessons learned and novel approaches to re-imagining the place of faculty members at the table, and the ways in which they can be supported to ensure they continue to strive toward innovative teaching methods and strategies for a shifting landscape in LIS education. The panelists will keep their lightening talks short to enable opportunities for audience interaction (through small-group conversations or breakout rooms (for virtual attendees)).
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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 | 0.024 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.049 | 0.025 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 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".