Interaction with an enabling information retrieval system: Modeling the user's decoding and encoding operations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With new interactive technology, we can increase user satisfaction by designing information retrieval systems that inform the user while the user is on-line interacting with the system. The purpose of this article is to model the information processing operations of a generic user who has just received an informative message from the system and is stimulated by the message into grasping at a higher understanding of his or her information task or problem. The model consists of three levels, each of which forms a separate subsystem. In the Perception subsystem, the user perceives the system message in a visual sense; in the Comprehension subsystem, the user must comprehend the system message; and in the Application subsystem, the user must (a) interpret the system message in terms of the user's task at hand, and (b) create and send a new message back to the system to complete the interaction. Because of the information process stimulated by the interaction, the user's new message forms a query to the system that more accurately represents the user's information need than would have been the case if the interaction had not taken place. This article proposes a device to enable clarification of the user's task, and thus his/her information need at the Application subsystem level of the model.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it