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Battle for the Best: What Works Today in Recruiting Top Technical Talent

2002· article· en· W326033908 sur OpenAlexaboutno aff
Phillip M. Perry

Notice bibliographique

RevueResearch-Technology Management · 2002
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueBig Data and Business Intelligence
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCorporationBattleService (business)BusinessThe InternetHuman resourcesPublic relationsMarketingManagementPolitical scienceWorld Wide WebComputer scienceEconomicsFinanceHistory
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

When a company in California wanted to hire top engineering talent last spring, it turned to a resource virtually unknown a year earlier: a web-based talent mart that automatically matches employer queries with blind resumes, then exchanges emails between an anonymous applicant and a potential employer until an offer sprouts from common ground. Maybe this Internet interplay lacked the human touch of an executive recruiter, but that didn't seem to bother John Uhran, vp of operations for the San Jose-based Cytaq. As a one-year-old start-up software engineering firm with only 18 employees, the company would have had a tough time luring away a top-level staff member from a major corporation using traditional head-hunting tactics. But by employing the web-based service, called eProNet, Uhran could fish a large sea from a small boat by posting the requirements of the available position on an electronic form. data were then matched against information provided anonymously by the members of the 22 alumni organizations that belong to the service. result? Cytaq lured its new vp of engineering, complete with a Stanford Ph.D., away from Compaq. Tracking the Elusive Candidate Cytaq story tells in small what is writ large everywhere in technical recruiting: Employers are modifying their recruitment tactics to infiltrate an increasingly competitive market for top candidates. Has the economic downturn lessened the pressure on recruiters by making more talent available? Apparently not. Maybe downsizing restocked the waters, but fat trout are still scarce. The challenge for growing employers is that the jobs they need to fill are not necessarily the ones that can be filled by people on the street, says Andy Chan, president of the San Mateo, California-based eProNet. It's a matter of hiring `the best person available' versus `the best person.' is a mismatch between talent needs and talent Chan does not expect this situation to change for quite a while. Demographic trends seem to indicate that over the next 15 years fewer people will be available to do the work available. So employers will need to continue to push to use innovative ways to acquire and retain talent. Indeed, the softening economy with its much-publicized layoffs can be a double-edged sword in the battle for the best. On the one hand, with an epidemic of wandering eye now infecting traditionally stable top employers have the chance to recruit good performers from the competition. Even people who may not be personally affected by a layoff may take a new look at the marketplace, says Frank Brady, chair of the HR Directors Network of the Industrial Research Institute (RTM's publisher), and manager of human resources for HRL Laboratories, a Malibu, California-based R&D laboratory co-owned by Boeing, General Motors and Raytheon. They would rather make the decision to leave, themselves, than have it forced upon them. On the other hand, employers need to watch their backs: Their own top talent may seek higher ground if they are not convinced their employer can avoid being overwhelmed by an economic flood. Additional prospects come from the army of individuals formerly employed by failed dot-coms. Yet some employers are putting these applicants under special scrutiny. There is talent out there, but many of these individuals lack the fundamental experience one obtains by working at a mature business, says the R&D director at one large Silicon Valley high-tech firm. Working at a three-year start-up does not buy you business experience. At best, you learn how to operate and succeed (or fail) in a 24/7 emergency mode. Companies, then, are still battling for the best. It is indeed tough to find great talent, says Rafik O. Loutfy, director of Xerox's Canadian research center. There is a lot of competition for people in certain fields such as software, system engineering, and mechanical engineering. …

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,085
score de la tête « metaresearch » (Gemma)0,138
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,085
Score d'incertitude au seuil0,447

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0850,138
Méta-épidémiologie (sens strict)0,0020,002
Méta-épidémiologie (sens large)0,0030,002
Bibliométrie0,0070,007
Études des sciences et des technologies0,0290,028
Communication savante0,0440,062
Science ouverte0,0080,014
Intégrité de la recherche0,0240,027
Charge utile insuffisante (le modèle a refusé de juger)0,0270,023

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.

Tête enseignante Opus0,213
Tête enseignante GPT0,381
Écart entre enseignants0,167 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2002
Routes d'admission1
Résumé présentoui

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