Bibliographic record
Abstract
The economic downturn is causing concern about a potential decline in training by business and an emphasis on the importance of maintaining training levels through the recession. This research brief reviews the nature of the concerns being expressed; the arguments for continued investment in staff development during recessionary periods; and the evidence on the effects that training may have on businesses ’ survival and growth during both recessionary periods and ‘normal ’ economic conditions. Context and Current Concerns There is a generally held view that training budgets are ‘one of the first to go ’ in a recession. An on-line poll of 120 learning and development managers, as long ago as June 2008, found that 44 % expected cuts in their training budgets over the next 12 months, while 54 % expected budgets to remain stable and just 2 % expected an increase1. More recently, and reliably, the British Chambers of Commerce Quarterly Economic survey of 5,000 Businesses found that, in Quarter 3 2008: • intentions to invest in training among manufacturing firms fell three points to their lowest level since 2003, while • intentions to invest in training among service industries plunged 10 point to +4%, a record low since Quarter 2 1997, the earliest period for which figures are available2. Skills policy-makers, HR managers, businessmen and unions are all arguing that the translation of such negative expectations or intentions into actual training budget cuts would be perverse-counter-productive for individual businesses and damaging to British economy. Sir Chris Humphries, Chief Executive of the UK Commission for Employment and Skills, argues that “too often in the past there has been a tendency for businesses to cut back on investing in
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.575 | 0.386 |
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; the direct Gemma label and the distilled Codex classifier 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".