Bibliographic record
Abstract
Given the recent growth in the field, some students in forensic anthropology may find it difficult to devise an affordable, suitable, and ideally publishable thesis topic. Taphonomic projects often can fill this role, as many do not require a large budget, are experimental in nature, thus giving students greater control over the data that they will collect, and on topics that have not received large amounts of research. The author presents a list of possible topics primarily in the field of forensic taphonomy and aimed at the MS/MA thesis level, although some proposed projects are larger or could be adapted for a PhD dissertation. The topics include taphonomic effects associated with carnivores, rodents, birds, reptiles, and plants and subaerial weathering; marine environments; thermal alteration; ritual and anatomical activity; cemetery and non-cemetery burials; bone detection; decomposition; and taphonomic procedures. The suggested topics also include factors that may make them more difficult to explore and information on which venues may be the most appropriate. The author hopes to promote taphonomic research in multiple subfields and encourage graduate students to pursue publication of their results.
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.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.082 | 0.038 |
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".