Future skills projections and analysis : Research report : April 2024
Bibliographic record
Abstract
UKCES -UK Commission for Employment and SkillsWF -Working Futures provide tailored advice to commissioners, developers and users.Guidance could include, but is not limited to:• How best to identify and engage with stakeholders and experts.• How to assess and improve the performance of difference methods.• How developers can frame outputs and results, and how these should be interpreted by users.• How to tailor the sophistication of the selected method appropriately, especially given user's needs and data limitations.We discuss this in further detail in Section 6: Findings and recommendations. There is a key role for a central economy-wide forecastA single, respected foundational forecast at a national level provides a focus for expert input and debate and enables cohesion across government.If consensus is built around this central forecast it can act as a 'starting point' that others can use and build on (e.g.sectoral bodies; regions; LSIPs).At the moment this role is filled by Working Futures.This forecast has a degree of trust and consensus around it as a central reference point and has users at the economywide and segment level.It is being developed as part of the Skills Imperative 2035 programme, for example to build in a more detailed skills taxonomy.The methodology is in line with similar forecasts produced internationally, such as the US 8 and Germany.9 Whilst there is no single alternative skills forecasting approach that appears superior in all dimensions to Working Futures, gaps have been identified that could improve Working Futures going forward.Some of these gaps could be developed as builds or add-ons without substantively changing the current approach, such as building in a process for stakeholder engagement and developing additional scenario analysis (see Section 6: Findings and recommendations for more detail).Other gaps in Working Futures are the common limitations across the UK evidence base described above (including a skills taxonomy, forecasting changes within occupations, and developing granularity).Addressing these gaps would likely require more substantive development such as new data collection and/or investigating the potential to use new or more innovative approaches and techniques at certain stages to complement the central model.8 Employment Projections (EP) program.9 The QuBe project. 11Recommendations flowing from our work Facilitating cohesion and information sharingRecommendation 1: Create a central repository for skills forecasts and related documentation and information, including signposting to relevant methodologies and datasets.Recommendation 2: Provide synthesis and associated commentary summarising the latest skills forecasts, and highlighting key gaps in the evidence base.Recommendation 3: Develop best practice guidance for how skills forecasts should be commissioned, developed and/or used.This could include guidance on: engagement with experts and incorporating this into a forecast; assessing accuracy; and the framing of results and how to use and interpret outputs. Deepening the role of Working FuturesRecommendation 4: Develop Working Futures to address the current gaps.This could involve developing add-ons to the current approach (e.g.stakeholder engagement and scenarios).This could also involve investigating the potential to use new methods and inputs at certain steps of the overall approach (e.g. using vacancy data and data from employers and/or using new methods alongside the core model, for example dynamic skills taxonomies).This would build further on Working Futures' existing position as a trusted central forecast.Recommendation 5: If Working Futures cannot feasibly be adapted to close key gaps, then an alternative new forecast method could be considered.User needs may be better met by a forecast method that can deliver on some of the evidence gaps we have highlighted in our Findings.These benefits should be weighed against the time and resource costs, and the risk that having multiple economy-wide forecasts could reduce cohesion.Recommendation 6: Develop a process for knowledge sharing and diffusion of information on the central forecast, for both segment-level and economy-wide users.Combined with recommendations 1-3, this will build consensus and encourage best practice use.11 Leitch Review of Skills (gov.uk) 12 UK Commission for Employment and Skills (gov.uk) 13 Sector Skill Development Agency 14 Employer skills survey: 2022 (gov.uk)
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.217 | 0.091 |
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".