Inner Circles and Outer Reaches: Local and Global Information-Seeking Habits of Authors in Acknowledgment Paratext.
Bibliographic record
Abstract
Introduction. This research investigates paratextual acknowledgements in published codices in order to study how relationships inform the information-seeking habits of authors, an understudied group in library and information science. Method. A purposive sample consisting of the books from the 2010 nominations list of the Canadian Governor General's Literary Awards was chosen. An in-hand examination of the books was performed to identify the acknowledgement paratext. The sum of these paratextual parts formed the dataset. Analysis. A qualitative content analysis of the acknowledgement paratext was performed. Throughout this inductive analysis, memos were used to record the creation and refinement of categories, as well as the emerging results. Results.The research reveals that authors rely on people as information resources as well as for moral and emotional support. Sources include personal allies, communities, and publishing staff. Libraries and informational professionals are generally absent from the acknowledgement paratext. Conclusion. Authors seek and find support in their local or personal circles and in global or distant horizons. An information model, based on Robert Darnton's communication circuit, is proposed. Information practitioners may be able to build on cues from the acknowledged relationships in order to tailor services to this group of users.
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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".