How do you write and present research well? Q4 – Do not metastasize with metadiscourse
Bibliographic record
Abstract
Abstract In the classic writing style, writers recognize that readers are competent and will recognize the truth as you lead them through your work. Soggy prose seeks “to argue for the truth.”[1] Your text should read like a conversation rather than a lecture. Match each sentence with one of the cardinal sins of writing to the left.[2,3] (Multiple choices possible) 1 Hedging b) In general these results show that a system with zero‐cost identities does not require centralized allocation of identities to encourage cooperation.[4] 2 Signposting f) In this section we shall evaluate the rate of recombination for nonequilibrium conditions.[5] 3 Redundant c) Here we report our new results on the samarium‐arsenide.[6] 4 Self‐conscious, 6 Boosting e) Whether established pests are suitable for attempted eradication is extremely controversial.[7] 5 Narcissism d) More recently researchers have attempted to quantify the effects of anxiety on foreign language learning.[8] 6 Boosting a) These results are extremely significant statistically and compare favorably with validation studies.[9]
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 | 0.022 | 0.142 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.008 |
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".