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Record W7052169554

“Put your mask on first before helping others”: Faculty members as a neglected population during Covid

2021· article· en· W7052169554 on OpenAlexaboutno aff

Bibliographic record

VenueIDEALS (University of Illinois Urbana-Champaign) · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsDignityPopulationHigher educationFlourishingPandemicCoronavirus disease 2019 (COVID-19)Psychological resilienceScholarship
DOInot available

Abstract

fetched live from OpenAlex

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)).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0740.000

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.

Opus teacher head0.017
GPT teacher head0.244
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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