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.<br>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.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.155 | 0.004 |
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; both teacher heads agree on what is shown here.
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".