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
Abstract Generally speaking, ‘focus’ refers to the portion of an utterance which is especially informative or important within the context, and which is marked as such via some linguistic means. It can be difficult to provide a single precise definition, as the term is used somewhat differently for different languages and in different research traditions. Most often, it refers to the linguistic marking of (i) contrast, (ii) question-answering status, (iii) exhaustivity, or (iv) discourse unexpectability. An illustration of each of these possibilities is given below. In English, the focus-marked elements (indicated below with brackets) are realized with additional prosodic prominence in the form of a strong pitch accent (indicated by capital letters). (i) An [AMERICAN] farmer met a [CANADIAN] farmer…(ii) Q: Who called last night?A: [BILL] called last night.(iii) Only [an ELEPHANT] could have made those tracks.(iv) I can’t believe it: The Ohioans are fighting [OHIOANS] ! The underlying intuition common to all these instantiations is that a focus represents the minimal information needed to convey an important semantic distinction. Focus can be signaled prosodically (e.g., in the form of a strong pitch accent), syntactically (e.g., by moving focused phrases to a special position in the sentence), or morphologically (e.g., by appending a special affix to focused elements), with different crosslinguistic focus marking strategies often carrying slightly different restrictions on their use. Example (i) evokes a set of two contrasting alternatives, {‘American farmer,’ ‘Canadian farmer’}, and the meaning ‘farmer’ is common to both members of the set. That is, within this evoked set of alternatives, ‘farmer’ is redundant, and it is the nationality of the farmers which differentiates the two people. Example (ii) exhibits a similar property. One of the standard theories of question semantics represents questions as sets of possible appropriate answers. For (ii), this would be a set of propositions like {‘Bill called last night, ‘Sue called last night,’ etc.}. As with (i), there is an evoked set of meanings whose members share some overlapping semantic material. Within this set, the verb phrase meaning ‘called last night’ is redundant, and it is the identity of the subject that serves to differentiate the true answer. Example (iii) demonstrates a relationship between focus and certain words like only. The sentence means something like ‘of all the animals who might have made these tracks, it must be an elephant.’ As with (i) and (ii), this involves a set of alternatives: the set of possible track makers. That the sentence serves to single out a unique member of this set as being the true track maker makes the subject an elephant a natural focus of the sentence. Finally, in (iv), we see that focus on ‘Ohioans’ is being used to contrast the semantic content of the sentence with some preconception, namely that Ohioans are unlikely fighters of Ohioans. Examples (iii) and (iv) point to more specific uses of focus in different languages. In Hungarian, so-called identificational focus, which is marked syntactically, requires an exhaustive interpretation, as if a silent only were present. And in some Chadic languages, a meaning of “discourse unexpectability,” as in (iv), is required to mark focus via syntactic or morphological means.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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