Metacognition: Your key to success
Bibliographic record
Abstract
Now is a good time to do some metacognition, i.e. to pause and think about what you did in the first quarter and what you want to do in the second quarter. By re-aligning yourself with your goals and values, you can be the best version of yourself for the rest of the year. In this chapter, we share some useful metacognition tools.Ngoku lixesha elifanelekileyo ukuze uyeke ukucinga ngokwenzileyo kwikota yokuqala nokuba ufuna ukwenza ntoni kwikota yesibini. Ngokuzilungelelanisa nezinto ozifunayo, ungalelona guqulelo lungcono kuwe ude uphele unyaka. Kwesi isahluko sabelana ngezixhobo zokucinga eziluncedo. Dis nou 'n kwaai tyd om stil te staan en te dink oor wat djy innie eerste term gedoen het en wat djy innie tweede term wil doen. As djy jouself remind van jou goals en wie djy is en wat djy kan bereik, sal djy die beste version van jouself wies vir die res vannie jaar. Die chêpter sal vi-jou 'n paar tips en tools gie wat vi-jou sal help om te reflect.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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".