Bibliographic record
Abstract
Introduction to Bitransitive Verbs In English, there are verbs that take two objects, a direct and an indirect object: ‘I gave the waiter a tip’, ‘I cooked him dinner’. The direct object is the thing (seldom a person) that the verb directly acts upon. The indirect object is usually an animate being for or to whom the action is done. The indirect object can be expressed in two ways. Both the direct and indirect objects sometimes appear a simple nouns or pronouns, and in this case, the indirect object precedes the direct one (as in the examples given). The indirect object can also be indicated with the prepositions ‘for’ or ‘to’, in which case it follows the direct object: ‘I gave a tip to the waiter’ or ‘I cooked dinner for him’. In terms of Nahuatl grammar, we might term the indirect object the beneficiary (this term is to be understood broadly, as the indirect object may be harmed rather than benefited by the action). In these instances, the Nahuatl verb can take two objects, one representing the regular direct object and the other the beneficiary. Such verbs are called bitransitive . Unlike the case with English, where word order clearly distinguishes which is which when the beneficiary appears without the preposition, the bitransitive verbs in Nahuatl make no such formal distinction. In the natural order of things, the direct object will be inanimate and the beneficiary animate, but this is by no means always the case.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".