Bibliographic record
Abstract
Life coaching is an emerging and ambiguous new profession. This study examines the information-rich worlds of three life coaches living in Toronto, Canada. Utilizing semi-structured interviews, in conjunction with Sonnenwald et al.’s (2001) Information Horizon Interview technique, this exploratory research offers a window into life coaches’ information exchange practices (Stebbins, 2001). The central research query guiding this study is: What are the information sources that life coaches rely on? The study yields both qualitative and quantitative findings, which were inductively analyzed using thematic analysis. First, it reveals that on their journeys to becoming life coaches, participants relied heavily on the insights of other life coaches. Next, life coaches share how they collect, share, and create resources for their clients. Finally, life coaches demonstrate how they utilize resources in many mediums and from many origins. This report adds to a burgeoning area of interest in the field of Library and Information Science (LIS), as it builds on recent dissertation research published by Klein (2022) about the information seeking practices of life coaches. Ultimately, this report diverges from Klein’s by introducing an alternative theoretical framework with which to make sense of life coaches’ information practices. Instead, it likens life coaches’ information practices to Willson’s (2021) “bouncing ideas” theory, whereby life coaching entails a back-and-forth exchange of ideas, questions, and goals that ultimately generates new information.
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 | 0.009 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.020 | 0.021 |
| Scholarly communication | 0.033 | 0.035 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.062 | 0.032 |
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".