Plotting Your Job Hunt: The Use of Visual Timeline for Investigating the Job Search Process
Bibliographic record
Abstract
Objective – This article discusses the use of a visual timeline activity in qualitative research investigating a process. This was part of a larger project exploring the experience of former library staff and their searches for academic librarian positions. It will also discuss the impact a visual method had in shaping the quality of the data. Methods – In 2023, the author conducted in-depth virtual interviews with 22 former library staff working in U.S.-based academic libraries about their experiences applying for academic librarian positions. A timeline worksheet was incorporated into the interviews, in which participants were asked to chronologically plot out their searches, as well as discuss significance of their selections. Both transcripts and timelines were analyzed using inductive thematic analysis, with the derived codes applied to both. Timelines were analyzed for visual connection to themes. Results – Participants used the timelines as a way to explain steps taken during their job searches, eventually leading to the start of their new jobs. Completing the activity helped participants recall moments from their searches during the interview. They also used the visual format to express the emotions they felt and their sense of passing time. Issues that arose while conducting the activity included some participants’ fears of “doing [the activity] wrong,” as well as limitations of the digital tool used to lead the interviews. Conclusion – Qualitative research in library science can benefit from the use of visual methods like timelines, especially for research on procedural aspects of working in academic librarianship. While practical matters such as extra time and material needs may hamper a researcher’s desire to use them, visual data can supplement oral interview transcripts.
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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 | 0.022 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".