School inspection update: June 2016: issue 7
Bibliographic record
Abstract
As we come towards the end of this academic year, I want to reflect on the positive developments since last September when we launched our common inspection framework.This is now well established in early years, maintained schools and academies, independent schools and further education and skills provisions.Additionally, the new short inspections of good schools and further education and skills providers have been well received.So far this year, Her Majesty's Inspectors have carried out over 1,200 short inspections of previously good schools.Of these short inspections, 36% converted to a full section 5 inspection, more than a quarter of which resulted in the school being graded outstanding.We stated last year that we fully expected a proportion of schools to remain good when inspectors converted to gather additional evidence and this has indeed been the case: over half of the converted short inspections have confirmed that the school remained good.Now that the model of short inspections is well established, we want to pilot the involvement of our Ofsted Inspectors (OIs) in leading short inspections as well as section 5 inspections.We will undertake a programme in the autumn term to train and deploy OIs with relevant inspection experience as lead inspectors.This will provide a wider pool of expertise and more flexibility for the delivery of the programme of short inspections.We will evaluate the pilot at the
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.136 |
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".