“Bouncing ideas” as a complex information practice: information seeking, sharing, creation, and cooperation
Bibliographic record
Abstract
Purpose “Bouncing ideas” is a phrase used colloquially to illustrate a way of advancing ideas in the workplace. While described by some as a key part of their information work, it has remained largely unexplored in the information science literature. As a metaphor used to depict information work, it describes a process of working on ideas in conjunction with others. This paper examines how early career academics use the term when describing their academic work. Design/methodology/approach This paper reports on one of the findings from a larger, in-depth study that examined the information behaviour of early career academics undergoing career transitions, which was carried out using constructivist grounded theory (CGT). CGT provides both a framework for the systematic collection (that included multiple interviews and check-ins with 20 early career academics) and analysis of the data (that consisted of multiple rounds of iterative, inductive coding). Findings The findings identify the component parts of bouncing ideas, which include three component in-formation activities – information seeking, information sharing and information creation – and are undertaken as cooperative information work (joint work for a shared purpose, but the benefits of the work may not be equal between participants). Originality/value Bouncing ideas is proposed as a complex information practice, defined as engaging in a temporary cooperative effort that involves social information exchange in order to gain help and/or support for an intellectual endeavour to create new information. The work identifies that more research into bouncing ideas is needed to more fully explore the distinct component behaviours that take place whilst bouncing ideas and the social conditions that foster this collaborative exchange.
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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 | 0.043 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.011 | 0.078 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".