<i>I feel like</i> and <i>it feels like</i>: Two paths to the emergence of epistemic markers
Bibliographic record
Abstract
Abstract The collocation I feel like has attracted American media attention for reportedly being newly ubiquitous (Baker 2013, Smith 2015, Worthen 2016). While I have proposed that it is becoming an epistemic marker in North American dialects of English (Brook 2011: 65), I have made this prediction of (it) feels like as well. The present study artificially restricts the conventional envelope of variation to evaluate what distinguishes these two phrases in vernacular Canadian English. I feel like is the more frequent by far, but (it) feels like shows a specialization for metaphorical subordinate clauses rather than concrete ones. I interpret this as a case of persistence (Torres Cacoullos and Walker 2009). Before the arrival of the like complementizer, the only predecessors to ’(it) feels like were (it) feels as if and (it) feels as though, and both as if and as though have a preference for metaphoricality (Brook 2014). I feel like was also preceded by options with ’as if and as though, but counterbalanced with that and Ø, which prefer concrete subordinate clauses (Brook 2014). The results attest to the value to be found in (cautiously) conducting a microscopic study of a corner of the envelope of variation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".