Future skills projections and analysis : Research report : April 2024
Notice bibliographique
Résumé
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)
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,217 | 0,091 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».