Building and Mobilizing Social Capital: A Phenomenological Study of Part-time Professors
Bibliographic record
Abstract
This paper explores the experiences of new part-time professors (instructors hired on a semester-by-semester basis that have been working at the institution for less than five years) and considers the phenomenon of how they connect with peers. It examines whether a lack of connection exists among part-time professors at the University of Ottawa and how this may affect their experience (i.e. teaching and career), lead to barriers to connection, and affect their social capital (i.e., their ability to access or use resources embedded in their social networks). Using Moustakas’ (1994) phenomenological approach for collecting and analyzing data and Creswell’s (2007) approach for establishing validity, we uncovered several thematic patterns in participants’ experience that indicate barriers to connection and affect the ability to access and mobilize social capital: Feeling uncertain or impermanent, isolated, overwhelmed, and like second-class citizens. The paper concludes that inadequate social capital may not only influence part-time professors – it may also have problematic implications for students, the department, and the University as a whole. Keywords: Social capital, barriers to communication, phenomenology, qualitative methods, part-time professors
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".