Comparing episodic and semantic interfaces for task boundary identification
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
Multi-tasking is a common activity for computer users. Many recent approaches to help support a user in multi-tasking require the user to indicate the start (and at least implicitly) end points of tasks manually. Although there has been some work aimed at inferring the boundaries of a user's tasks, it is not yet robust enough to replace the manual approach. Unfortunately with the manual approach, a user can sometimes forget to identify a task boundary, leading to erroneous information being associated with a task or appropriate information being missed. These problems degrade the effectiveness of the multi-tasking support. In this thesis, we describe two interfaces we designed to support task boundary identification. One interface stresses the use of episodic memory for recalling the boundary of a task; the other stresses the use of semantic memory. We investigate these interfaces in the context of software development. We report on an exploratory study of the use of these two interfaces by twelve programmers. We found that the programmers determined task boundaries more accurately with the episodic memory-based interface and that this interface was also strongly preferred.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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