Your Neighbor is a Writer: Curating a Local E-book Collection Kady - PowerPoint PPT Presentation

Jan 22, 2023 •138 likes •426 views

Your Neighbor is a Writer: Curating a Local E-book Collection Kady Ferris, Electronic Content Librarian Poll: How are you offering local content? Physical books? E-books? Both? Neither? 2 Katie Grindeland, author, The Gifts We

Your Neighbor is a Writer: Curating a Local E-book Collection Kady Ferris, Electronic Content Librarian
Poll: How are you offering local content? ● Physical books? ● E-books? ● Both? ● Neither? 2
Katie Grindeland, author, The Gifts We Keep “Without this program through the Multnomah County Library my book would still be sitting at home in a binder somewhere. This program helps bridge the gap between enthusiastic writers and willing readers. The reception to my work has been extremely validating, and I'm exceptionally grateful for this opportunity. And now you can imagine a cartwheeling emoji or something equally culturally relevant.” 3
4
Library Writers Project: 3 Years Running... 5
Poll Question #1 Results: How are you offering local content? 6
Poll: What platforms are you using? ● OverDrive or Cloud Library? ● Biblioboard/SELF-e? ● Own platform? ● Other? ● N/A 7
Why Local Collections (and Why E-books)? Unique Library as curator Exposure for the author Access & ease
The Practical: Deciding on a Platform OverDrive vs. Biblioboard vs. ?? Where the readers are Cost Trade off: Staff time 9
Poll Question #2 Results: What platform are you using? 10
Poll: What are the barriers to offering? ● Staff time? ● No technical resources? ● Perceived quality of content? ● Saying no to community members? ● Other? 11
The Practical: The Technical Set-up Smashwords portal Submission form Parameters: Local, fiction 20% Sample 12
The Practical: The Review Process Google Drive is your friend Staff reviewers Scoring In person reviewer check-ins 13
14
The Practical: The Review Process Google Drive is your friend Staff reviewers Scoring In person reviewer check-ins 15
The Practical: Adding the Books Notify authors Purchasing MARC Records Featured titles 16
Featured Titles 17
Poll Question #3 Results: What are the barriers? 18
Expectation vs. Reality: Submission Quality 19
Expectation vs. Reality: Time 20
Expectation vs. Reality: Reception Submissions Selected Circ 2015 140 38 7177 2016 70 19 4371 2017 42 10 741+ 21
Marketing 22
Marketing 23
Scaling Down Advertise the process, leave it open ended More submission parameters Only take what you can handle 24
What’s Next? Expanding submission criteria Publisher partnership + print edition Library Music Project 25
Author Impact “When I tell someone I wrote a book, instead of just saying it hasn't been published and isn't available, I am able to say that it is available on the Multnomah County Library website as an e- book.” “Increased sales and readership. Reader Reviews increased. My audio books got attention as well.” “We write to be read. The Writers Project found us readers while we waited for Random House to call.” 26
Thank you! Questions? Contact: kadyf@multcolib.org 27

Recommend

CSCI 447/547 MACHINE LEARNING Outline Nearest Neighbor K-Nearest Neighbor Algorithm

Nearest Neighbor CSCI 447/547 MACHINE LEARNING Outline Nearest Neighbor K-Nearest Neighbor Algorithm Note: Slides were adapted from David Sontag, New York University (who adapted them from Vibhav Gogate, Carlos Questrin, Mehryar

200 views • 16 slides

Scaling IPv6 Neighbor Discovery Ben Mack-Crane ( ben.mackcrane@huawei.com ) Overview of Neighbor

Scaling IPv6 Neighbor Discovery Ben Mack-Crane ( ben.mackcrane@huawei.com ) Overview of Neighbor Discovery Protocol IPv6 nodes on the same LAN use Neighbor Discovery (RFC4861) to to find routers and discover link and network parameters,

310 views • 10 slides

Nearest Neighbor Classification Machine Learning 1 This lecture K-nearest neighbor

Nearest Neighbor Classification Machine Learning 1 This lecture K-nearest neighbor classification The basic algorithm Different distance measures Some practical aspects Voronoi Diagrams and Decision Boundaries What is the

893 views • 57 slides

Learning From Data Lecture 16 Similarity and Nearest Neighbor Similarity Nearest Neighbor M.

Learning From Data Lecture 16 Similarity and Nearest Neighbor Similarity Nearest Neighbor M. Magdon-Ismail CSCI 4100/6100 My 5-Year-Old Called It A ManoHorse The simplest method of learning that we know. Classify according to similar

331 views • 16 slides

Example Im at work, neighbor John calls to say my alarm is ringing, but neighbor Mary doesnt

Example Im at work, neighbor John calls to say my alarm is ringing, but neighbor Mary doesnt call. Sometimes its set o ff by minor earthquakes. Is there a burglar? Variables: Burglar , Earthquake , Alarm , JohnCalls , MaryCalls Network

849 views • 55 slides

Proximity in the Age of Distraction: Robust Approximate Nearest Neighbor Search Sariel Har-Peled

Proximity in the Age of Distraction: Robust Approximate Nearest Neighbor Search Sariel Har-Peled Sepideh Mahabadi UIUC MIT Nearest Neighbor Problem Nearest Neighbor Dataset of points in a metric space (, ) , e.g.

967 views • 81 slides

BAYES AND NEAREST NEIGHBOR BAYES AND NEAREST NEIGHBOR CLASSIFIERS CLASSIFIERS Matthieu R Bloch

BAYES AND NEAREST NEIGHBOR BAYES AND NEAREST NEIGHBOR CLASSIFIERS CLASSIFIERS Matthieu R Bloch Tuesday, January 21, 2020 1 LOGISTICS LOGISTICS TAs and Office hours Monday: Mehrdad (TSRB 523a) - 2pm-3:15pm Tuesday: TJ (VL C449 Cubicle D) -

362 views • 12 slides

On Optimal Neighbor Discovery Philipp H. Kindt philipp.kindt@tum.de SIGCOMM19, Beijing CH

On Optimal Neighbor Discovery Philipp H. Kindt philipp.kindt@tum.de SIGCOMM19, Beijing CH 918/5-1 - Slotless Neighbor Discovery Neighbor Discovery in MANETs Pairwise Groupwise Pairwise in Groups Realizing Neighbor Discovery

747 views • 19 slides

High-Dimensional Nearest Neighbor Search High-Dimensional Nearest Neighbor Search Who?

High-Dimensional Nearest Neighbor Search High-Dimensional Nearest Neighbor Search Who? About Cliqz and me What? Problem statement Why? Applications How? Exact solutions in low dimensions Approximate

690 views • 19 slides

NEAREST NEIGHBOR RULE Jeff Robble, Brian Renzenbrink, Doug Roberts Nearest Neighbor Rule

NEAREST NEIGHBOR RULE Jeff Robble, Brian Renzenbrink, Doug Roberts Nearest Neighbor Rule Consider a test point x. x is the closest point to x out of the rest of the test points. Nearest Neighbor Rule selects the class for x with the

573 views • 33 slides

Simultaneous Nearest Neighbor Search Piotr Indyk Robert Kleinberg MIT Cornell Sepideh

Simultaneous Nearest Neighbor Search Piotr Indyk Robert Kleinberg MIT Cornell Sepideh Mahabadi Yang Yuan MIT Cornell Nearest Neighbor Dataset of points in a metric space (, ) 6/17/2016 2 Nearest Neighbor

883 views • 69 slides

Nearest neighbor methods Lecture 11 David Sontag New York

Nearest neighbor methods Lecture 11 David Sontag New York University Slides adapted from Vibhav Gogate, Carlos Guestrin, Mehryar Mohri, & Luke Zettlemoyer Nearest Neighbor Algorithm

311 views • 20 slides

Average - case Lower Bounds for Approximate Near - Neighbor fs om Isoperimetric Inequalities

Simple Average - case Lower Bounds for Approximate Near - Neighbor fs om Isoperimetric Inequalities Yitong Yin Nanjing University Nearest Neighbor Search ( NNS ) metric space ( X, dist) query x X database access y = ( y 1 , y 2 , . . . , y

384 views • 26 slides

My New Neighbor LESSON 7 Your Response to the Lesson What was most interesting in the Bible

My New Neighbor LESSON 7 Your Response to the Lesson What was most interesting in the Bible story? What activity was most enjoyable? What new things did you learn? Activity A Find-a-Neighbor Party How do you like to celebrate with others?

357 views • 22 slides

Intrinsic t-Stochastic Neighbor Embedding for Visualization and Outlier Detection A Remedy

Intrinsic t-Stochastic Neighbor Embedding for Visualization and Outlier Detection A Remedy Against the Curse of Dimensionality? Erich Schubert, Michael Gertz October 4, 2017, Munich, Germany Heidelberg University t-Stochastic Neighbor

691 views • 32 slides

MATTHEW 5:43 You have heard that it was said, Love your neighbor and hate your enemy.

MATTHEW 5:43 You have heard that it was said, Love your neighbor and hate your enemy. LEVITICUS 19:18 Do not seek revenge or bear a grudge against anyone among your people, but love your neighbor as yourself. I am the Lord. MATTHEW

355 views • 13 slides

Improving Neighbor Discovery with Slot Index Improving Neighbor Discovery with Slot Index

Improving Neighbor Discovery with Slot Index Improving Neighbor Discovery with Slot Index Synchronization Synchronization Shuaizhao Jin , Zixiao Wang , Wai Kay Leong , Ben Leong , Yabo Dong , Dongming Lu Zhejiang

712 views • 46 slides

Benchmarking Neighbor Discovery Problems Bill Cerveny March 12,

Benchmarking Neighbor Discovery Problems Bill Cerveny March 12, 2013 History Suggested by Ron Bonica at IETF 85 BMWG meeKng Neighbor Discovery (ND)

284 views • 10 slides

Nearest Neighbor and Locality-Sensitive Hashing Nearest Neighbor Set Similarity

Nearest Neighbor and Locality-Sensitive Hashing Nearest Neighbor Set Similarity Locality-Sensitive Hashing Document Similarity Philip Bille Nearest Neighbor and Locality-Sensitive Hashing Nearest Neighbor Set Similarity

623 views • 17 slides

Nearest Neighbor Classification Seed classification by area and What should we compactness

Nearest Neighbor Classification Seed classification by area and What should we compactness predict for unlabeled Lecture 6 KNN and Decision Trees test points (stars)? Nearest neighbor classification: predict CS 335 label of

203 views • 3 slides

Nearest Neighbor Classifiers CSE 4308/5360: Artificial Intelligence I University of Texas at

Nearest Neighbor Classifiers CSE 4308/5360: Artificial Intelligence I University of Texas at Arlington 1 The Nearest Neighbor Classifier Let X be the space of all possible patterns for some classification problem. Let F be a distance

512 views • 24 slides

Nearest neighbor classification in metric spaces: universal consistency and rates of convergence

Nearest neighbor classification in metric spaces: universal consistency and rates of convergence Sanjoy Dasgupta University of California, San Diego Nearest neighbor The primeval approach to classification. Given: a training set { ( x 1 , y

926 views • 20 slides

1 Nearest Neighbor Heuristic Nearest Neighbor Heuristic Start at some point p 0 p 1 p 0

Algorithms: definition &6($OJRULWKPVDQG &RPSXWDWLRQDO&RPSOH[LW\ Procedure to accomplish a task or solve a well-specified problem $OJRULWKPVDQG(IILFLHQF\ Well-specified: know what

51 views • 3 slides

Learning: Nearest Neighbor, Perceptrons & Neural Nets Artificial Intelligence CSPP 56553

Learning: Nearest Neighbor, Perceptrons & Neural Nets Artificial Intelligence CSPP 56553 February 4, 2004 Nearest Neighbor Example II Credit Rating: Name L R G/P Classifier: Good / A 0 1.2 G Poor B 25

847 views • 49 slides