Highlighting the present state of biomechanics in shoe research (2000–2023)
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
Footwear science research has seen a roughly 10-fold increase in publications over the last 20 years. This commentary will focus on the three primary research themes of this time frame: methodological developments, running-related injuries, and performance. Within each theme, we summarise the knowledge generated through the substantial increase in publications over the last couple of decades. The methodological developments highlight both improvements in data analysis techniques as well as changes in how we measure variables of interest. Running-related injury prediction paradigms have evolved significantly during these years, which affect how we recommend moving forward in the field. Substantial excitement has filled the performance research field, as we discuss how the advent of Advanced Footwear Technologies altered the research questions and approaches. The undeniable growth in the field over in recent years can be attributed to a strong foundation of knowledge, nurtured by a curiosity to obtain understanding through holistic approaches. The community has embarked on the next stage of the journey, armed with new data collection tools and analytical methodologies, with the objective to better understand the effect of novel footwear design on performance enhancement and injury prevention.
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 | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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