Bibliographic record
Abstract
The present paper analyses the inferential use of the Spanish synthetic future form, i.e. examples in which the future is used as an inferential marker. The examples are retrieved from the search engine GlossaNet, more precisely, from the Spanish daily newspapers El Pais and El Mundo. This study does not aim to be a quantitative one. It is a qualitative study where the data are used to verify/refute the theoretical basis. It is, for example, not my intention to show in how many cases of a certain total amount of uses the Spanish synthetic future form is used to express inference. It is argued that the label “inferential future” (instead of “epistemic future”) should be preferred. Additionally, the synthetic future is shown to convey inferences of different strengths: sera + sin duda (“it will/must be + without a doubt”), for instance, represents a stronger inference, while with sera + probablemente (“it will/must be + probably”) a weaker inference is expressed.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".