African American Vernacular English: Categories of Necessity in a Language that Refuses to be Standard

African American Vernacular English (AAVE) has been spoken by African Americans for centuries but has only recently been acknowledged as a distinct dialect. It is often used in tandem with Standard English (SE) by users of SE, through a concept referred to as code-switching. Although linguists have done substantial work to validate AAVE, there is an incomplete understanding of why the dialect developed and, in particular, the functions the dialect serves for its speakers. In order to begin the work of discovering why AAVE developed the specific features it manifests, I synthesized other linguists’ observations into a taxonomy of five categories that account for most of the dialect’s unique features. My project elaborates on the functions of the categories of tense/mode variation, negation, absence, prosody/ pronunciation, and what Zora Neale Hurston calls “the will to adorn” in AAVE, in comparison to SE

African American Vernacular English: A Language Necessarily Adorned

African American Vernacular English (AAVE) has been spoken by African Americans for centuries but has only recently been acknowledged as a distinct dialect. It is often used in tandem with Standard English, through a concept referred to as code-switching. Although linguists have done substantial work to validate AAVE, there is an incomplete understanding of why the dialect developed, and, in particular, what functions the dialect serves for its speakers. In order to begin the work of discovering why AAVE developed the specific features it manifests, I synthesized other linguists’ observations into a taxonomy of five categories that account for most of the dialect’s unique features. My project elaborates on the functions of the categories of tense/mode variation, negation, absence, prosody/pronunciation, and what Zora Neale Hurston calls “the will to adorn” in AAVE, in comparison to Standard English

Reflections on that-has-been : Snapshots from the students-as-partners movement

EDITORIAL NOTE (Alison): The idea for this multipart reflective essay emerged from first author Christel Brost’s reflections on her experience of striving to develop a students-as-partners approach within the context of a summer institute and then back at her home institution. To aid reflection on these experiences, Christel used Roland Barthe’s construct of that-has-been, which she explains below, to examine several “mental snapshots” of her experiences and what those mean for her personally and for students-as-partners work. Inspired by the vivid, emotion filled representation of Christel’s “snapshots,” we (co-editors of reflective essays for the journal, Anita Ntem and Alison Cook-Sather) invited participants from two other venues to share their reflections within the same frame. Authors of each section of this essay use Barthes’ construct to “zoom in” on different moments and lived experiences of partnership, creating mental snapshots from three students-as-partners venues. The first venue is the Change Institute at the May 2017 International Summer Institute on Students as Partners held at McMaster University, in Hamilton, Ontario, Canada. The second is the May 2017 Pedagogic Partnership Conference held at Lafayette College in, Easton, Pennsylvania, in the United States. The third is the June 2017 RAISE International Partnership Colloquium held at Birmingham City University in Birmingham, England.Non peer reviewedFinal Published versio

Vers une détection automatisée des comportements délétères des porcs en élevage

Aggressive behaviours harm the health and welfare of pigs, and in turn negatively affect the productivity of farms. INRA and CEA are collaborating in the European project PIGWATCH (ERANET Anihwa) to develop an automatic system to detect harmful behaviours such as fighting. The system is based on sensors and algorithms of artificial intelligence. In this study, the system (developed by CEA-LETI) included a triaxial accelerometer and was fixed on the ears of the pigs. It was connected to an Android application, enabling data to be transferred to a smartphone via Bluetooth. Twelve pigs were fitted with the system. Their activity was recorded regularly with a video camera for two months. Pig behaviours, especially fighting, were scored from about 24 hours of recordings. Signals from the sensors were marked as a function of the behaviours observed. Mathematical characteristics of the data that differed according to the behaviours displayed at the time were extracted. We then developed an algorithm using these characteristics to detect behaviours automatically. Currently, the algorithm can detect aggressive behaviours with a sensitivity of 41% and a specificity of 87%. An improvement is expected by increasing the data basis. Preliminary analyses of variations during the day in behavioural activities obtained from predictions of the system show typical variations in agreement with preceding studies, with high levels of activity in the early morning and afternoon. These results are encouraging for the potential of the system to automatically record behaviours on farms.Les comportements agressifs sont délétères et affectent la santé et le bien-être des porcs ainsi que la productivité des élevages. Dans le cadre du projet européen PIGWATCH (ERANET Anihwa), l’INRA et le CEA travaillent au développement d’une technique automatisée, basée sur des capteurs et des algorithmes d’intelligence artificielle, pour détecter les comportements délétères de type bagarres. Le CEA-LETI a développé un dispositif porté à l’oreille incluant un accéléromètre triaxial. Le dispositif est connecté à une application Android pour l’acquisition des données sur un smartphone via une communication Bluetooth basse consommation. Douze porcs ont été équipés avec ce dispositif. Leur activité a été enregistrée et observée par caméra, à intervalles réguliers, durant 2 mois. Les comportements et notamment les bagarres ont été identifiés à partir d’environ 24 heures d’enregistrements vidéo et les signaux issus des capteurs ont été marqués en accord avec ces observations. Les caractéristiques mathématiques pertinentes des signaux pour discriminer les comportements observés ont été extraites. Dans une seconde étape, ces caractéristiques ont été utilisées dans des algorithmes pour détecter automatiquement les comportements. Actuellement, l’algorithme est capable de détecter les agressions avec une sensibilité de 41% et une spécificité de 87%. Des progrès sont attendus en augmentant la base de données. L’analyse préliminaire des variations dans la journée de l’activité comportementale à partir des prédictions de l’algorithme révèle des variations qui rejoignent les études précédentes avec des pics d’activité en début de matinée et d’après-midi. Ces résultats sont très encourageants quant à la validation du système et son utilisation future pour l’enregistrement automatique des comportements en élevage