A Communication Model that Bridges Knowledge Delivery between Data Miners and Domain Users
Bibliographic record
Abstract
Findings generated from data mining sometimes are not interesting to the domain users. The problem is that data miners and the domain users do not speak the same language, so human subjectivity towards the domain users’ own fields of knowledge affects the understanding of knowledge generated from data mining. This paper proposes a communication model based on the reference services model in the field of library science in order to bridge the communications between data miners and domain users. The creation of a data liaison specialist role in the data mining team aims at understanding the subjectivity as well as the thinking process of both parties in order to translate knowledge between the two fields and deliver findings to domain users. Through five steps-”data interview, pre-mid evaluation, post-mid evaluation, knowledge delivery, and follow up-”the data liaison specialist can achieve effective knowledge synthesis and delivery to the domain users.
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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 | 0.035 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".