Obtaining Copyright Permission to Digitize Published Works Remains a Significant Barrier
Notice bibliographique
Résumé
A review of: George, Carole A. “Testing the Barriers to Digital Libraries: A Study Seeking Copyright Permission to Digitize Published Works.” New Library World 106.1214/1215 (2005): 332-42. Objective – To assess the effectiveness and efficiency of the copyright permission-seeking process and to suggest improvements in order to improve outcomes. Design – Workflow study. Setting – Carnegie Mellon University Libraries, Pittsburgh, PA, USA. Sample – A random sample of titles published 1999-2001 was selected from the library’s circulating collection. After eliminating duplicates, technical reports, theses, dissertations, and missing items, the sample comprised 337 titles. Of these titles, 70% were books, and 56% were from commercial publishers. From this a working sample of 273 titles was derived, comprising those titles protected by copyright and with the rights owner clearly indicated. About 73% of this working sample appeared to be out-of-print; their median publication year was 1981. Method – In this two year study (1999-2001), a random sample of books was selected, and pertinent bibliographic and copyright holder information researched and recorded. Permission letters were sent and, six weeks later, follow-up letters were sent to non-respondents. The letter allowed respondents four options: Grant full permission to digitize the work and provide unrestricted Web access; Grant permission to digitize the work and provide read-only Web access, limited to Carnegie Mellon University users; Declare that they do not hold the rights, and hopefully provide information to identify and locate the actual rights holder; Deny permission for digitization. Results were then recorded and analyzed. Main results – Of the 273 letters mailed, a clear ‘yes’ or ‘no’ reply was obtained for just over half (52%) of the documents. Sixteen percent of the rights holders could not be found (the letter was returned, or a referral proved impossible to locate and contact). Another 25% of the copyright holders simply did not reply, and 7% were otherwise problematic. Of the 143 ‘yes’ or ‘no’ responses, 54% denied permission, while 46% granted permission. (Note: these percentage figures appear to be erroneously reversed in Table 1 of George’s article.) Therefore, of the overall working sample of 273 titles, permission to digitize was obtained for only 24% of the titles. A substantial portion of the permissions (41 of 66, or 62%) carried some restriction. This represents 15% of the total working sample. Only a few restriction requests were deemed too great to make use of the permission. Commercial publishers who made up 58% of the working sample granted permission at the lowest rate (13%). Response time averaged three months from the time the initial letter was sent until a ‘yes’ or ‘no’ response was received. Negative responses averaged a bit longer than positive responses (101 days to 124 days). However, some of this time was attributable to delays in issuing follow-up or redirected request letters (a step required in 60% of cases), owing to the limited staff resources at Carnegie Mellon. The copyright ownership had changed in 23% of the sample, requiring more than one and up to three different addresses to be contacted before a response was received or the effort was terminated. Conclusions – The study concluded that the permission rate would remain low unless additional efforts were made in the permission-seeking process (e.g., personal contacts in addition to letters and emails), or unless more selective approaches were employed (e.g., targeting non-commercial publishers). It also concluded that the process to seek copyright permissions was neither quick nor easy, suggesting the need for dedicated staff time and a readily accessible database of publisher contact information. As a result, subsequent projects have improved their permission-seeking process, focusing on more non-commercial publishers or older publication dates, and asking publishers for blanket consent for all of their out-of-print titles.
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 | 0,035 | 0,198 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,007 | 0,009 |
| Communication savante | 0,017 | 0,014 |
| Science ouverte | 0,003 | 0,008 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,027 | 0,009 |
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 ».