A framework for representing navigational patterns as full temporal objects
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
Navigational patterns have applications in several areas including: web personalization, recommendation, user-profiling and clustering, etc. Most existing works on navigational pattern-discovery give little consideration to the effects of time (or temporal trends) on navigational patterns. Some recent works have proposed frameworks for partial temporal representation of navigational patterns. This paper proposes a framework that models navigational patterns as full temporal objects that may be represented as time series. Such a representation allows a rich array of analysis techniques to be applied to the data. The proposed framework also enhances the understanding and interpretation of discovered patterns, and provides a rich environment for integrating the analysis of navigational patterns with data from the underlying organizational environments and other external factors. Such integrated analysis is very helpful in understanding navigational patterns (e.g., E-commerce sites may integrate the trend analysis of navigational patterns with other market data and economic indicators). To achieve full temporal representation, this paper proposes a navigational pattern-discovery technique that is not based on pre-defined thresholds. This is a shift from existing techniques that are driven by pre-defined thresholds that can only support partial temporal representation of navigational patterns.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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