Reflecting on Two Decades of Information Horizons Theory and Method: Applications and Innovations
Bibliographic record
Abstract
ABSTRACT Information horizons is a theory and method that embraces behavioral, cognitive and social aspects of information seeking, sharing, and use. As the first method originating in information science that uses a graphical data collection technique (Hartel, 2016), it has served as the underpinning methodology for numerous investigations for over 20 years. The method is often applied in studies of diverse communities not traditionally included in research, such as lower socio‐economic populations. Information horizons is also valuable in teaching master's and doctoral students about information behavior and importance of theoretical constructs. For example, over 700 master's students at the University of Toronto have completed exploratory, empirical studies using the method. A strength of the method is its extensibility; researchers have extended and adapted the method for use with different populations and to investigate different types of information behavior. This panel will explore the varied applications of the information horizon method, engage the audience in open discussion about the information horizons method for research and teaching, and explore how theoretical and methodological approaches can be more effectively shared across the information science and related communities.
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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 | 0.129 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.016 | 0.031 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".